Collection of DRAGONS Workshops
Basic DRAGONS Workshop
Learning Objectives
This material is built for a 2-hr workshop, planning on 90 minutes for the workshop and 30 minutes for questions and helping participants.
In this workshop, the participants will gain a basic understanding about 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
reduce.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.
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, Installation Guide.
Follow the Python 3 and DRAGONS instructions, IRAF is not needed. Make sure that you do the steps in the “Configure DRAGONS” section.
Important
This workshop is written for DRAGONS v3.1
The participants will need to download the data package to run the examples and the exercises:
http://www.gemini.edu/sciops/data/software/datapkgs/niriim_tutorial_datapkg-extended-v1.tar
Download it and unpack it somewhere convenient.
cd <somewhere convenient>
tar xvf niriim_tutorial_datapkg-extended-v1.tar
bunzip2 niriimg_tutorial/playdata/example1/*.bz2
The datasets are found in the subdirectory niriimg_tutorial/playdata/example1/, and
we will work in the subdirectory named niriimg_tutorial/playground.
Note
If you are attending a live workshop, it is advisable to also download the preprocessed data package in case you encounter technical difficulties that prevents you from running the full demo yourself. The content of that package will allow you to continue with most of the exercises using those preprocessed data. Download the “just-in-case” package at:
https://drive.google.com/file/d/1sJd8bHUFZ3-dNVT3_VllcqplnoSZr4LJ/view?usp=sharing
You do not need to do anything with it now other then keeping it somewhere safe. Hopefully, everything will run smoothly and you will not need it.
What is DRAGONS
What it is
DRAGONS is a platform for the reduction and processing of astronomical data. It is a Python meta-package that includes infrastructure for automation and algorithms for processing astronomical data. Currently, it focuses on the reduction of Gemini data, but hooks are available for expansion to data from other observatories.
Now really, what is DRAGONS?
With a little bit of guidance from you, DRAGONS will reduce your Gemini data in a standard way, rapidly, with little fuss. With a bit more guidance from you, DRAGONS will let you optimize the reduction for your specific sources and science objectives.
DRAGONS is operated through the reduce command from the shell, or the
Reduce class from a Python script. You choose. For clarity, we will refer
to reduce only from now on, but it’s the same thing. A call to reduce
activates the “Recipe System” which is what automates everything.
When reduce is called, the first FITS file is opened and identified, then
the libraries of algorithms collected as “primitives” and “recipes” will be
searched and the best matches will be selected and run on the data.
The FITS files are opened with the astrodata facility. When a FITS file is
opened with astrodata, it “knows about itself”. For example, it knows that
it is a NIRI file and an IMAGING FLAT. This is how the Recipe System will
know which recipes and which primitives to load and run.
The other important component of the DRAGONS automation infrastructure is the
calibration manager. It is operated with the caldb command. (Again,
there’s an API too.) This accesses a lightweight local sqlite database that
will store information about your locally-processed calibrations. When a
primitive is run and needs a calibration, the system will automatically
identify the best matched processed calibration and use it, you do not have to
specify it on the command line. (Though, you can if you really want to.)
Then there’s a series of utilities for sorting through the data and learning about the primitives and the recipes.
Here’s what DRAGONS can look like from a user’s point view:
dataselect *.fits --tags=FLAT -o flat.lis
dataselect *.fits --expr="object='mycooltarget'" -o sci.lis
caldb init
reduce @flat.lis
caldb add *_flat.fits
reduce @sci.lis
The steps are:
Create your lists of input data.
Initialize your calibration database.
Reduce your calibrations and upload the info to the database (the upload can be automated).
Reduce your science, it will pick up the calibrations by itself.
As straightforward as a reduction can be, it can be customized to match your needs. The options to the primitives can be adjusted, the recipes themselves can be adjusted.
In this basic introduction to DRAGONS, we will explore all this and learn how to have DRAGONS do our bidding.
What it is not
DRAGONS is not a data analysis package. DRAGONS prepares the data for analysis but does not offer tools to do the analysis. Also, DRAGONS is also not a replacement for IRAF.
To inspect your data, to do any sort of analysis like photometry, measuring redshift, calculating equivalent width, etc, you will have to find another tool. DRAGONS does provide a primitive to display an image to ds9, but it will just display it. Similarly, there is a tool to plot a 1D spectrum, and again, it will just plot it, it does not offer any measurement capabilities.
Any analysis-type tools are beyond the scope of DRAGONS.
Components in action
The diagram below illustrates how the components communicate with each other
and in particular how the astrodata tags and descriptors are passed around
to make decisions about the best matched recipe, primitive sets, and
calibrations.
What are Recipes and Recipe Libraries
A DRAGONS recipe is a set of instructions, called primitives, that processes data in a certain way. Depending on the recipe, the input data can be in any state of processing. Most often though, people will want to process Gemini raw data into a master calibration or a processed calibrated output.
A recipe library is a collection of recipes. The recipes in a library have one thing in common: they all apply to the same type of data. When DRAGONS search for a matching recipe for some input data, it searches for a recipe library. In each library, one recipe is set as the default recipe. To use the others, the user needs to specify the name of the non-default recipes.
Let’s look at examples and tools.
Recipes
This is what a recipe looks like:
def reduce(p):
p.prepare()
p.addDQ()
p.removeFirstFrame()
p.ADUToElectrons()
p.addVAR(read_noise=True, poisson_noise=True)
p.nonlinearityCorrect()
p.darkCorrect()
p.flatCorrect()
p.separateSky()
p.associateSky(stream='sky')
p.skyCorrect(instream='sky', mask_objects=False, outstream='skysub')
p.detectSources(stream='skysub')
p.transferAttribute(stream='sky', source='skysub', attribute='OBJMASK')
p.clearStream(stream='skysub')
p.associateSky()
p.skyCorrect(mask_objects=True)
p.detectSources()
p.adjustWCSToReference()
p.resampleToCommonFrame()
p.stackFrames()
p.writeOutputs()
A recipe is a Python function that calls “primitives” sequentially. The user does not need to know Python to understand more or less what is being done to the data when the recipe is run. From the recipe above, we can read that the data will be corrected for dark current, for flat field effects, the sky background will be subtracted and the frames will be stacked. The p argument is the primitive set that matches the input. We will talk about that in the next chapter.
Exploring recipes
showrecipes can be used to see which recipe DRAGONS with run by default given the input data. The tool’s basic usage signature is:
showrecipes fitsfile_name.fits
To see more options, try showrecipes -h.
Exercise - Recipes 1
The file N20160102S0373.fits, in the playdata/example1 directory, is a raw
“lamp-on” flat.
Get the default recipe for a NIRI flat displayed on the terminal
Find the name of the default recipe.
[Solution]
Recipe libraries
Recipes are stored in recipe libraries, or in Python language, a module. A recipe library can have one or more recipes in them. One recipe is identified as the default recipe.
When DRAGONS searches for a recipe, it is actually searching for a matching recipe library. Once found, it will run the default recipe, unless instructed otherwise by the user.
The recipe libraries are associated with the input files by matching
astrodata tags (Astrodata User Manual). The tags are qualifiers like “NIRI”,
“IMAGE”, “FLAT”. The tags of the first file in the list of inputs are used
by the matching algorithm. Each recipe library is assigned a set of tags that
defines which type of data this library is for.
Exercise - Recipes 2
The file N20160102S0423.fits in the playdata/example1 directory is a raw dark.
Find the location of the recipe library on your disk.
What are the tags assigned to the input file?
What are the tags assigned to that recipe library?
[Solution]
As mentioned above, a recipe library can have more than one recipe. To see
the list of recipes, showrecipes has the option --all. Then, to see the
sequence of primitives for one of those recipes, there’s the option -r.
DRAGONS has the concept of “reduction mode”. There are three modes:
the science quality mode, “sq”, the quicklook mode, “ql”, and the quality
assessment mode, “qa”. You can safely ignore the “qa” mode, it is used
exclusively at the observatory, at night, to help with the assessment of the
sky conditions and the resulting quality of the data. The mode can be
specified in showrecipes with the -m flag. The default is “sq”.
To see all the flags and option, showrecipes -h.
Exercise - Recipes 3
For the file N20160102S0270.fits in the playdata/example1 directory:
List all matching recipes. Note the “sq”, and the “qa” recipes.
Show the
makeSkyFlatrecipe. (“sq” is the default.)Show the
reducerecipe for “qa” mode.
[Solution]
What are primitives
Primitives and primitive sets
A primitive is a data reduction step involving a transformation of the data or
providing a service. By convention, the primitives are named to convey the
scientific meaning of the transformation. For example biasCorrect will
remove the bias signal from the input data.
A primitive is always a member of a primitive set. It is the primitive set that gets matched to the data by the Recipe System, not the individual primitives.
Technically, a primitive is a method of a primitive class. A primitive class
gets associated with the input dataset by matching the astrodata tags. Once
associated, all the primitives in that class, locally defined or inherited,
are available to reduce that dataset. We refer to that collection of
primitives as a “primitive set”.
All the primitives are currently found in the Python package geminidr.
In there, there is a large inheritance tree of primitive classes from very
generic to very specific.
For example, NIRI images will be associated with the NIRIImage
primitive set. This set contains all the primitives it needs through
primitives defined in NIRIImage itself and through its inheritance
tree. We illustrate that tree in Figure 1.
Figure 1: NIRIImage inheritance tree.
Once the primitive set is identified, DRAGONS will send that to the
selected recipe as that p argument we saw in the previous chapter.
Primitive input parameters
Also attached to a primitive set are the input parameters for each primitives and the defaults appropriate for that primitive set, that is for that type of data.
For the same generic primitive, the default values for input parameters for NIRI data can be different from the defaults applicable to GMOS data. Even the set of available input parameters can be different, though we try to keep things as uniform as possible.
We will learn later how to customize the values of input parameters. The first step however is to see what the input parameters are and what their default values are. This is done with the showpars command.
Because the input parameters can be different for different type of data, the command requires the name of file, and then of course the name of the primitive you want to learn about.
The command signature is as follow:
showpars filename primitive_name
Exercise - Primitives 1
Using the science frame N20160102S0270.fits located in directory
playdata/example1, find the answer to the questions below. First, show the
recipe for that file to see the list of primitives. (Hint: showrecipes)
What is the default operation used to combine the sky frames during sky correction?
Which parameter from which primitive do I need to modify to turn off the dark correction?
Which parameter from which primitive do I need to modify to change the rejection method during the final stacking of the frames? What are the available options?
[Solution]
Demo imaging
To get ourselves oriented and get a feel for how data is processed with DRAGONS, we will run a full reduction on our sample data. The full tutorial for reducing NIRI data can be found at NIRI Imaging Data Reduction Tutorial.
Here, we go through the steps with a bit less discussion than in the full tutorial as the purpose is simply to provide some context and an idea of a typical data reduction flow to those who have not experience it before.
The observations are of an extended source, a nearby galaxy. The instrument used is NIRI. The science sequence is a series of dithers on target with full offset to sky. We will create a master dark, a master flat, a reduced flux standard, and finally create a calibrated, sky-subtracted stack of the science observations.
Let’s run all the steps.
Set up the local calibration manager
Create the file lists
Create the calibration files (dark, flat)
Reduce the flux standard
Reduce the science observations
cd <where_the_data_package_is>/niriimg_tutorial/playground
Set up the local calibration manager
We will discuss the local calibration manager in a later chapter.
The calibration manager is the first thing to set up when starting on a data reduction project. It provides the automated calibration association.
The file to pay attention to is: ~/.dragons/dragonsrc (DRAGONS v3.1+)
Edit that file to contain the following:
[calibs]
databases = <where_the_data_package_is>/niriimg_tutorial/playground/cal_manager.db get
In this configuration, with the “get” flag, DRAGONS will retrieve the processed
calibration automatically from that database. You could add “store” after the
“get” to have DRAGONS add the processed calibrations to the
database automatically (effectively running caldb add for you). Here we
will add the calibrations it manually.
About the path for databases, we recommend the directory you are using
to work on the data. But it can be anywhere.
Once the configuration file is set up, initialize the database. This needs to be done only once per database.
caldb init
This creates a new file, cal_manager.db, in the directory specified by
databases.
Create the file lists
The user has to sort the files. DRAGONS is a “User-in-the-Loop” pipeline. We have some tools to help. We will have a closer look at them later.
We have long darks that match the science, a series of lamp-on and lamp-off flats, a set of on-target dithered observations for a flux standard, and a set of on-target dithered and off-target dithered observations for the science target.
dataselect ../playdata/example1/*.fits --tags DARK --expr='exposure_time==20' -o darks20s.lis
dataselect ../playdata/example1/*.fits --tags FLAT -o flats.lis
dataselect ../playdata/example1/*.fits --expr='object=="FS 17"' -o stdstar.lis
dataselect ../playdata/example1/*.fits --expr='object=="SN2014J"' -o target.lis
Add static BPM to calibration database
The static BPMs are downloaded from the archive (but included here in our data package).
caldb add ../playdata/example1/bpm_20010317_niri_niri_11_full_1amp.fits
Create the master dark and the master flat
Dark
Create the master dark and add it to the calibration manager.
reduce @darks20s.lis
caldb add N20160102S0423_dark.fits
The @ character before the name of the input file is the “at-file” syntax.
We will look into this later.
Flat
Create the master flat from the lamp-on and lamp-off flats and add it to the calibration database.
reduce @flats.lis
caldb add N20160102S0373_flat.fits
Reduce the flux standard
This is short series of on-target dither observations. Darks are not obtained for NIRI flux standard so we turn that step off. The flat will be picked up from the calibration database.
reduce @stdstar.lis -p darkCorrect:do_cal=skip
Reduce the science observations
The sequence is a set of dither on target with full offsets to sky. DRAGONS will sort them during the sky correction. All calibrations are picked up automatically from the calibration database. Because the target fills the field of view, we need to turn off the scaling of the sky frames.
reduce @target.lis -p skyCorrect:scale_sky=False
Explore data
Once we have reduced our data, we probably want to look at. In fact, even before we start the reduction, we should look at the raw data in case of issues.
DRAGONS is not an analysis package. But it does offer a basic way to display images.
The display and inspect primitives can be used to visually inspect
the data with ds9. Those are primitives not recipes, yet reduce can
run individual primitives.
Note
ds9 must be launched by the user ahead of running the primitive.
Display
To display an image to ds9:
reduce -r display N20160102S0271_stack.fits
By default, the saturated pixels are marked. This can be turned off by
setting the threshold parameter to “None”.
Inspect
It is recommended to inspect the raw data prior to reducing the data. A quick
visual inspection can catch obviously bad data, or flag some frames for
more detailed inspection. Having to run display on each frame individually
would be painful. The primitive inspect can be used here. It
accepts a list of file and will display each frame in sequence, adding a short
pause between the frames. The length of the pause can be controlled with
the pause parameter.
reduce -r inspect @stdstar.lis
Exercise - Explore 1
Display
N20160102S0271_stack.fitsto buffer 2 and turn off the saturation marking.Reduce the time between frames to 1 second in
inspect.
Hint: reduce file.fits -r primitive -p primitive:parameter=value
[Solution]
Accessing table content
Some output files will have table information. Once written to disk as a FITS
file, the tables are accessible as any FITS files. A common table is a list
of sources detected by the primitive detectSources.
Let say that we want to get position of the sources in the flux standard stack
we produced in the previous chapter. First, we run detectSources since
it was not run after the stack was created.
reduce -r detectSources N20160102S0296_stack.fits
This will add a table with object coordinates, magnitudes, etc. The output file
is N20160102S0296_sourcesDetected.fits. Use your preferred tool to access
the FITS table named OBJCAT from that output. Or you can use DRAGONS’
astrodata to access the table as an Astropy Table.
1import astrodata
2import gemini_instruments
3
4ad = astrodata.open('N20160102S0296_sourcesDetected.fits')
5ad.info()
Filename: N20160102S0296_sourcesDetected.fits
Tags: GEMINI IMAGE NIRI NORTH PREPARED SIDEREAL
Pixels Extensions
Index Content Type Dimensions Format
[ 0] science NDAstroData (1195, 1195) float32
.variance ndarray (1195, 1195) float32
.mask ndarray (1195, 1195) uint16
.OBJCAT Table (29, 43) n/a
.OBJMASK ndarray (1195, 1195) uint8
6ad[0].OBJCAT
<Table length=29>
NUMBER X_IMAGE Y_IMAGE ERRX2_IMAGE ... REF_MAG REF_MAG_ERR PROFILE_FWHM PROFILE_EE50
int32 float32 float32 float64 ... float32 float32 float32 float32
------ --------- ---------- ---------------------- ... ------- ----------- ------------ ------------
1 638.74554 7.358276 9.678123990684229e-06 ... -999.0 -999.0 -999.0 -999.0
2 52.5378 15.138286 0.0007821264390706773 ... -999.0 -999.0 0.0 3.5255525
3 500.59454 1146.9167 0.012262252608200411 ... -999.0 -999.0 4.513517 6.1514697
4 879.47473 1098.4614 2.762548642098426e-05 ... -999.0 -999.0 4.222008 5.0135765
5 99.23588 1012.4829 7.044837003541005e-06 ... -999.0 -999.0 3.5682483 4.5748672
6 67.8556 1023.36334 0.0036609541234138973 ... -999.0 -999.0 3.7424104 4.9484925
7 654.096 918.19916 2.5297123639012174e-05 ... -999.0 -999.0 4.6524267 4.829588
8 855.02795 830.9594 6.732778544641382e-06 ... -999.0 -999.0 6.675581 6.2657185
9 1026.4033 835.157 0.0433908185530153 ... -999.0 -999.0 4.068429 12.851359
10 380.1143 726.31464 0.00041709156417067195 ... -999.0 -999.0 4.370194 4.4451947
11 994.73755 721.932 0.012686314238205688 ... -999.0 -999.0 4.6524267 9.30962
... ... ... ... ... ... ... ... ...
18 854.6116 352.42896 1.5660364975379077e-05 ... -999.0 -999.0 4.222008 4.193287
19 522.56934 317.01556 0.0012430236331253412 ... -999.0 -999.0 3.5682483 3.738548
20 936.59576 242.70079 7.264929326425364e-06 ... -999.0 -999.0 4.068429 4.1155357
21 811.8014 225.92892 8.23835737153326e-05 ... -999.0 -999.0 3.9088202 4.1611533
22 478.92426 217.57622 0.02797088088581828 ... -999.0 -999.0 8.812923 -999.0
23 479.3753 206.3997 0.022581652547939684 ... -999.0 -999.0 12.257336 12.230416
24 478.64758 194.86026 0.009814622938623417 ... -999.0 -999.0 2.7639532 15.229653
25 521.57794 209.82019 0.029336651973282495 ... -999.0 -999.0 3.1915383 16.864668
26 479.6435 152.71979 3.3380840555892644e-06 ... -999.0 -999.0 3.9088202 3.7304442
27 955.26416 150.0823 7.081766055952015e-05 ... -999.0 -999.0 3.7424104 4.108046
28 561.57697 151.73418 0.021476893612064937 ... -999.0 -999.0 6.675581 9.169356
29 129.58281 107.06496 0.0009807660866281128 ... -999.0 -999.0 3.3851376 3.6767333
See the astropy documentation for information about how to operate on
Table objects: https://docs.astropy.org/en/stable/table/.
For more on astrodata, see the Astrodata User Manual.
Local calibration manager
The local calibration manager is the tool that will allow DRAGONS to find the most appropriate processed calibration for the data being reduced.
The local calibration manager contains all the same rules as the Gemini
Observatory Archive, but works locally, with your files, without internet, and
alongside DRAGONS to make the calibration association as seamless as possible.
As we have seen in the demo earlier, we create the master calibrations, we add
them to the database, and when DRAGONS (reduce) needs one, it gets matched
and retrieved automatically.
The system uses a lightweight sqlite database that will not store the
data file, it will store only information about the file, like its
location on disk, and key astrodata tags and descriptors.
Warning
If you move or delete a processed calibration on disk after having entered it in the database, the calibration manager will no longer be able to find it. The database does not contain the file, just information about the file.
See the caldb documentation for more information, including information about the programming interface.
Configuration and initialization
Warning
The information provided here applies to DRAGONS version 3.1 (and newer). The configuration and features of the calibration manager in the previous versions, 3.0 and older, are substantially different. Please refer to a previous version of this workhop if you are using DRAGONS 3.0 or older,
Configuration
The behavior and configuration of the local calibration manager are controlled
in the file ~/.dragons/dragonsrc. When you first install DRAGONS, you
will have to create that directory and that file.
If you ran the demo earlier, the dragonsrc file should look like this:
[calibs]
databases = <where_the_data_package_is>/niriimg_tutorial/playground/cal_manager.db get
The value of databases can be any existing path on your machine. The
calibration database will be created, and then expected to be in that directory.
The database name can be anything, here we name it cal_manager.db for
continuity with pre-v3.1 DRAGONS.
We recommend setting up a new database for each observing project. It helps keep things clean and avoids calibrations from other programs being unexpectedly picked up. (If they were picked up, they would be a match, but it might get confusing if you are expecting another calibration to be picked up.)
Here is an example of a dragonsrc with several databases entries.
Note that at all times, only one databases directive is active, the
others are commented out.
[calibs]
databases = /Users/klabrie/data/tutorials/niriimg_tutorial/playground/cal_manager.db get
#databases = /Users/klabrie/data/tutorials/gmosimg_tutorial/playground/cal_manager.db get
#databases = /Users/klabrie/data/workspace/GMOS540feature/cal_manager.db get
Initialization
Once your dragonsrc is set up, you can create a new database with
caldb init
If the database already exists, that command will refuse to work. This ensures
that you will not wipe the database accidentally. The init is required
only once. If you already have the database created, maybe even populated,
you can switch back to it simply by ensuring databases points to it.
Exercise - Caldb 1
Create a new database in the niri_tutorial directory. Keep the
playground entry that should already be there, but deactivate it.
Confirm with ls that a new cal_manager.db has been created. You
can also use caldb config to confirm that the new database is active.
[Solution]
Usage
The usage is fairly basic. One can list the content, add content,
and remove content. DRAGONS will retrieve content from the database. If
the “store” flag is set, DRAGONS will also automatically add content to the
database.
Warning
In DRAGONS version 3.0 and older, DRAGONS cannot add content
automatically, the user must add the processed calibrations to the
database with an explicit caldb add.
To verify which database is currently active, use the config option:
caldb config
To see the content of the database:
caldb list
To add information about a processed calibration to the database:
caldb add name_of_file.fits
And finally, to delete information about one file from the database:
caldb remove name_of_file.fits
remove will only remove the entry in the database, it will not remove
the file on disk.
Exercise - Caldb 2
Do Exercise - Caldb 1 first.
Add the flat field from the demo to the new calibration manager created in the first exercise.
Show the content of that database is indeed just that file.
Reactivate the original database (the one in
playgroundthat we used for the demo) and list its content. Both the dark and the flat should now be listed.
[Solution]
Using reduce
The reduce command (and the Reduce class if you are using the
programming interface) is what drives DRAGONS. The command has a lot of
options. We have used a few already. Let’s review those and show a few more.
First, to see the list of options available, use the -h flag:
reduce -h
Setting the suffix of the final outputs
It is not possible to set the name of the final output. This seems odd, but here is why. There might be more than one output, there can be dozens, it depends on the recipe.
We are still exploring solutions to this problem.
In the meantime, the user can set the suffix of the outputs. Each primitive
has a meaningful suffix attached to it, for example _sourcesDetected for
the primitive detectSources. The output of a reduce call will get
the suffix assigned to the last primitive of the recipe.
If you wish to override that, you can set the --suffix option.
reduce @targets --suffix=_mytest
Exercise - “reduce” 1
Run the primitive detectSources on the flux standard stack
(N20160102S0296_stack.fits) and set the output suffix to
_ILoveDRAGONS.
Hint: To specify a recipe or a primitive to run, remember the -r flag.
[Solution]
Customizing parameters
We have seen earlier how to check the available input parameters and their default settings for a given frame and primitive. The command showpars does that. For example:
showpars ../playdata/example1/N20160102S0363.fits normalizeFlat
The syntax to change the value of an input parameter is as follow:
reduce file.fits -p primitive:parameter=value
Exercise - “reduce” 2
Reduce the flats from the demo again but this time set the scaling for
normalizeFlat flat to mean. To avoid overwriting our “real” processed
flat, let’s set the suffix to _exercise2.
Hint: cat flats.lis to get the name of a flat to use with showpars.
[Solution]
Running non-default recipes
A recipe library often contains several recipes. One recipe in the library
is set as the default library. If one wants to run a different library,
its name can be specified on the command line using the -r flag.
As we have seen before to see the list of all available recipes, we can use the
showrecipes command with the --all flag.
showrecipes ../playdata/example1/N20160102S0270.fits --all
Input file: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata/example1/N20160102S0270.fits
Input tags: {'GEMINI', 'NORTH', 'NIRI', 'UNPREPARED', 'SIDEREAL', 'IMAGE', 'RAW'}
Recipes available for the input file:
geminidr.niri.recipes.sq.recipes_IMAGE::alignAndStack
geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.sq.recipes_IMAGE::reduce
geminidr.niri.recipes.qa.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.qa.recipes_IMAGE::reduce
geminidr.niri.recipes.sq.recipes_IMAGE::alignAndStack
geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.sq.recipes_IMAGE::reduce
The strings sq and qa refer to the reduction mode. The qa mode,
Quality Assessment, is used internally at Gemini. General users will be
using the sq, Science Quality, recipes.
Warning
The last three “sq” recipes
in the list above are really the “ql”, quicklook, recipes. This a known
bug (circa April 2023). The NIRI quicklook recipes are identical to
the science recipes and are just “Python-imported” from the science module,
and that import trips the current implementation of showrecipes.
The reduction mode is sq by default. To change that one uses the --qa
or --ql flags with reduce. We will be using the science quality
recipes here so we do not need those flags in this version of workshop.
The syntax to select the specific recipe we want to use is:
reduce file1.fits file2.fits -r recipename (optional --ql/--qa)
The -r option can also be used to primitives as we have seen elsewhere.
Just type the name of the primitive instead of the recipe:
reduce file1.fits file2.fits -r primitivename
Another use of the -r option is to run personal recipes rather than the
ones distributed with DRAGONS. We will show how that is done in the next
chapter.
Exercise - “reduce” 3
Run the makeSkyFlat recipe on the science frames. Set the suffix
to _skyflat. Note that because
the target fills the field-of-view of the science frames, the sky flat
in this particular case will not be usable, but it’s okay, we are just
exploring the reduce command-line here.
[Solution]
Overriding the calibration selection
If you wish to force DRAGONS to use a specific processed calibration, overriding
the automatic selection, you can use the --user_cal flag. Here is a
usage example.
reduce file1.fits file2.fits --user_cal processed_arc:my_arc.fits
Exercise - “reduce” 4
In the demo, we reduced the flux standard as follow:
reduce @stdstar.lis -p darkCorrect:do_cal=skip
Modify this command to allow the dark correction to use the processed
dark we used for the science frame, N20160102S0423_dark.fits.
[Solution]
The “at”-file
We have use the “at”-file already. That @ symbol followed by the name
of the file list, that’s an “at”-file. They are very convenient to pass a
list of files, but their use is not limited to list of files.
Anything following the reduce command can be put in a file and prefixed
with the @ symbol on the reduce command line.
Let say that we have a very customized call to reduce:
reduce @stdstar.lis -r makeSkyFlat --suffix _skyflat \
-p stackFrames:reject_method=minmax stackFrames:operation=median \
skyCorrect:scale=False --user_cal processed_dark:N20160102S0423_dark.fits
That would be rather tedious to type again and again, or to edit to experiment with the parameter values. Also, when experimenting, it might be nice to keep of record of what is being attempted.
All this can be written to a file and passed to reduce with @.
Let’s write that file, let’s call it params, any name will do:
# I can add comments
-r makeSkyFlat # in-line comments too
--suffix _skyflat
-p
stackFrames:reject_method=minmax
stackFrames:operation=median
#stackFrames:operation=mean # I can comment out options to try something else
skyCorrect:scale=False
--user_cal processed_dark:N20160102S0423_dark.fits
# the file is completely free-form
Then to use that file:
reduce @stdstar.lis @params
The “at”-files can even be nested, for example I could add @stdstar.lis
to my params file:
@stdstar.lis
-r makeSkyFlat
--suffix skyflat
-p
stackFrames:reject_method=minmax
stackFrames:operation=median
skyCorrect:scale=False
--user_cal processed_dark=N20160102S0423_dark.fits
And just call @params, the @stdstar.lis will be expanded as if it had
been on the command line.
reduce @params
Customize Recipes
Sometimes the DRAGONS recipes are not quite what is needed. It is possible to customize a recipe and have DRAGONS run it. The easiest way to customize a recipe is to find the most appropriate one, copy it over and edit it.
Here we will show you how to write intermediate results to disk for inspection.
The first step is to find a recipe and copy it to the local directory. Let’s find the default NIRI science imaging recipe.
showrecipes ../playdata/example1/N20160102S0270.fits
The recipe name and the recipe library location returned will look like this:
...
Input recipe: reduce
...
Recipe location: /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_IMAGE.py
...
Note
The file might end with .pyc instead of .py. This is the
compiled file, the code file is in the same location and named .py. That
is the file we will copy over. It is not clear at this time why the .pyc
is sometimes returned instead of the .py filename.
cp /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_IMAGE.py .
You should now have a file named recipes_IMAGE.py in your current directory.
Let’s rename it for clarity:
mv recipes_IMAGE.py myNIRIrecipes.py
Use your favorite editor to open myNIRIrecipes.py to edit the recipe named
reduce. We will save some intermediate results: the flat corrected files
and the sky corrected frames before alignment and stacking. To do that we
need to add writeOutputs after flatCorrect and skyCorrect.
p.prepare()
p.addDQ()
p.removeFirstFrame()
p.ADUToElectrons()
p.addVAR(read_noise=True, poisson_noise=True)
p.nonlinearityCorrect()
p.darkCorrect()
p.flatCorrect()
p.writeOutputs()
p.separateSky()
p.associateSky(stream='sky')
p.skyCorrect(instream='sky', mask_objects=False, outstream='skysub')
p.detectSources(stream='skysub')
p.transferAttribute(stream='sky', source='skysub', attribute='OBJMASK')
p.clearStream(stream='skysub')
p.associateSky()
p.skyCorrect(mask_objects=True)
p.writeOutputs()
p.detectSources()
p.adjustWCSToReference()
p.resampleToCommonFrame()
p.stackFrames()
p.writeOutputs()
return
Note that some of the primitives in the recipe have parameters set in the recipe itself. Parameter values set in a recipe will always take precedence. Those cannot be changed by the user from the command line.
Now that we have an edited recipe that will write the flat corrected and the
pre-alignment sky corrected frames, how do we run it? With -r of course!
But the syntax is just a touch different. We need to specific both the
name of our recipe library (myNIRIrecipes) and the name of the recipe
(reduce).
reduce @target.lis -r myNIRIrecipes.reduce
The new intermediate outputs have the suffixes _flatCorrected and
skySubtracted.
Exercise - Custom Recipe 1
Edit the master flat recipe to write the pre-normalized flat to disk, and to display the master flat at the end of the recipe.
Hint: cat flats.lis to get the name of a flat to use with showrecipes
to get the location and name of the NIRI flat recipe library.
[Solution]
Tools
DRAGONS offers some tools to help with the bookkeeping. We have used
dataselect already to create lists, we used showpars and showrecipes
to check the primitive parameter settings and the recipes.
Here we will explore dataselect a bit more, and introduce showd and
typewalk.
Additional information about the tools can be found in the Supplemental Tools chapter of the Recipe System Users Manual.
typewalk
The oddly-named typewalk tool allows the user the list the astrodata tags
(formerly “types”) of all the files in a directory, and can recurse (“walk”)
through subdirectories. It can also be used to select data on tags, but we
recommend using the much more flexible dataselect for that.
To see the type of data files that we have in playdata/example1 and see which
tags are available for selection:
typewalk --dir ../playdata/example1
directory: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata/example1
N20160102S0270.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0271.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0272.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0273.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0274.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0275.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0276.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0277.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0278.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0279.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0295.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0296.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0297.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0298.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0299.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0363.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0364.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0365.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0366.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0367.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0368.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0369.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0370.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0371.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0372.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0373.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0374.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0375.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0376.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0377.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0378.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0379.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0380.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0381.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0382.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0423.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0424.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0425.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0426.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0427.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0428.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0429.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0430.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0431.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0432.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0463.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0464.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0465.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0466.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0467.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0468.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0469.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0470.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0471.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0472.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
From that output we see the darks with their DARK tag, the flats with the
FLAT tags, some with LAMPON, some with LAMPOFF. The science frames
do not have the CAL (for calibration) tag.
showd
typewalk shows tags, showd shows the values of astrodata descriptors.
The list available descriptor is included in an appendix of the Astrodata User Manual.
The syntax is:
showd -d descriptor_name filenames
showd -d descriptor1,descriptor2,descriptorN filenames
The default is to print on the terminal. A comma-separate list can be
produced with the --csv flag.
Exercise - Tools 1
Get the exposure time, filter name, and UT date of all the
observations in playdata/example1. Use the descriptors list from the
Astrodata User Manual.
[Solution]
dataselect
dataselect is a powerful tool to create list of exactly the files you want
based on tags and descriptors values.
You can include tags, exclude tags, and use Python comparison operators on the descriptors.
Here are a few examples.
Select only darks:
dataselect ../playdata/example1/*.fits --tags DARK
Select only non-darks:
dataselect ../playdata/example1/*.fits --xtags DARK
Select lamp-on flats:
dataselect ../playdata/example1/*.fits --tags FLAT,LAMPON
Select any frames with exposure time of 10 seconds:
dataselect ../playdata/example1/*.fits --expr="exposure_time==10"
Select 20-second darks:
dataselect ../playdata/example1/*.fits --tags DARK --expr="exposure_time==20"
Select any frames observed on or after 2016-01-03:
dataselect ../playdata/example1/*.fits --expr="ut_date>='2016-01-03'"
We can combine dataselect and showd. Here we are going to get the
observation type, as defined in the Gemini Observing Tool (OT), for those
frames observed on or after 2016-01-03:
dataselect ../playdata/example1/*.fits --expr="ut_date>='2016-01-03'" | showd -d observation_type
The expression in --expr is a Python expression using the Python syntax.
Note the double == for equality in the examples above. Using a single =
is a common mistake.
Also, be careful with the quotes. The external quotes must be different from the internal quotes used around strings.
Finally, the expression can use the and and or logical operators.
Exercise - Tools 2
Knowing that the science frames and the flux standard have an observation
type of ‘OBJECT’, and that the science frames observation class is
“science” while the flux standards are “partnerCal”, create a list of
science frames and a list of flux standards without using the object
names. Send the output to showd to print the object name.
Descriptors: observation_type, observation_class, object.
[Solution]
Resources
The DRAGONS documentation is available on “readthedocs” at https://dragons.readthedocs.io/
DRAGONS and all its dependencies are installed with conda. Installation instruction and a lot of information about how to use DRAGONS are provided in the Recipe System Users Manual.
The list of descriptors is found in Astrodata User Manual.
There are a series of tutorials available on the main DRAGONS portal. They cover reduction of imaging data for each of the current facility imagers.
The code is available on github at: https://github.com/GeminiDRSoftware/DRAGONS
Request for help should be done via the Gemini Helpdesk system using the Topic “DRAGONS”.
Solutions to exercises
Solutions to Recipe and recipe libraries exercises
Solution to Exercise - Recipes 1
The showrecipes command is used on the file. Highlighted below is the
name of the default recipe, makeProcessedFlat, and the primitives that
will be run.
showrecipes ../playdata/example1/N20160102S0373.fits
Recipe not provided, default recipe (makeProcessedFlat) will be used.
Input file: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata/example1/N20160102S0373.fits
Input tags: ['GCALFLAT', 'NORTH', 'AT_ZENITH', 'NON_SIDEREAL', 'AZEL_TARGET', 'RAW', 'IMAGE', 'GCAL_IR_ON', 'NIRI', 'GEMINI', 'UNPREPARED', 'LAMPON', 'CAL', 'FLAT']
Input mode: sq
Input recipe: makeProcessedFlat
Matched recipe: geminidr.niri.recipes.sq.recipes_FLAT_IMAGE::makeProcessedFlat
Recipe location: /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_FLAT_IMAGE.py
Recipe tags: {'FLAT', 'IMAGE', 'CAL', 'NIRI'}
Primitives used:
p.prepare()
p.addDQ()
p.addVAR(read_noise=True)
p.nonlinearityCorrect()
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.makeLampFlat()
p.normalizeFlat()
p.thresholdFlatfield()
p.storeProcessedFlat()
Solution to Exercise - Recipes 2
Again showrecipes is used. The recipe location, the input file tags, and the recipe tags are highlighted.
showrecipes ../playdata/example1/N20160102S0423.fits
Recipe not provided, default recipe (makeProcessedDark) will be used.
Input file: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata/example1/N20160102S0423.fits
Input tags: ['NON_SIDEREAL', 'AT_ZENITH', 'NIRI', 'NORTH', 'UNPREPARED', 'AZEL_TARGET', 'GEMINI', 'RAW', 'DARK', 'CAL']
Input mode: sq
Input recipe: makeProcessedDark
Matched recipe: geminidr.niri.recipes.sq.recipes_DARK::makeProcessedDark
Recipe location: /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_DARK.py
Recipe tags: {'NIRI', 'DARK', 'CAL'}
Primitives used:
p.prepare()
p.addDQ(add_illum_mask=False)
p.addVAR(read_noise=True)
p.nonlinearityCorrect()
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.stackDarks()
p.storeProcessedDark()
Solution to Exercise - Recipes 3
The option --all is used to show all the recipes. Note in the module
path the sq and the qa. The recipe names can be the same but their
content will differ depending on the reduction mode selected. Default is
always sq, science quality.
The last three “sq” recipes are really the “ql” recipes. This a known
bug (circa April 2023). The NIRI quicklook recipes are identical to
the science recipes and are just “Python imported” from the science module,
and that import trips the current implementation of showrecipes.
showrecipes ../playdata/example1/N20160102S0270.fits --all
geminidr.niri.recipes.sq.recipes_IMAGE::alignAndStack
geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.sq.recipes_IMAGE::reduce
geminidr.niri.recipes.qa.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.qa.recipes_IMAGE::reduce
geminidr.niri.recipes.sq.recipes_IMAGE::alignAndStack
geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.sq.recipes_IMAGE::reduce
To see the content of a specific recipe, name it with the -r flag.
showrecipes ../playdata/example1/N20160102S0270.fits -r makeSkyFlat
Finally, to print the content of the quality assessment, “qa”, recipe
named reduce (also the default in that qa library), use the
-m flag:
showrecipes ../playdata/example1/N20160102S0270.fits -r reduce -m qa
Solutions to Primitives exercises
Solution to Exercise - Primitives 1
The first step is get yourself familiarized with the primitive names. This can be done by looking at the recipe.
showrecipes ../playdata/example1/N20160102S0270.fits
p.prepare()
p.addDQ()
p.removeFirstFrame()
p.ADUToElectrons()
p.addVAR(read_noise=True, poisson_noise=True)
p.nonlinearityCorrect()
p.darkCorrect()
p.flatCorrect()
p.separateSky()
p.associateSky(stream='sky')
p.skyCorrect(instream='sky', mask_objects=False, outstream='skysub')
p.detectSources(stream='skysub')
p.transferAttribute(stream='sky', source='skysub', attribute='OBJMASK')
p.clearStream(stream='skysub')
p.associateSky()
p.skyCorrect(mask_objects=True)
p.detectSources()
p.adjustWCSToReference()
p.resampleToCommonFrame()
p.stackFrames()
p.writeOutputs()
Question 1:
This question is about sky correction. The primitive skyCorrect is
a good bet.
showpars ../playdata/example1/N20160102S0270.fits skyCorrect
...
operation 'median' Averaging operation
Allowed values:
wtmean variance-weighted mean
mean arithmetic mean
median median
lmedian low-median
...
The default for combining the sky frames is median.
Question 2:
The second question asks which paramter controls whether or not the dark
correction will be run. Let’s look at darkCorrect.
showpars ../playdata/example1/N20160102S0270.fits darkCorrect
Dataset tagged as {'NORTH', 'IMAGE', 'NIRI', 'UNPREPARED', 'SIDEREAL', 'GEMINI', 'RAW'}
Settable parameters on 'darkCorrect':
========================================
Name Current setting
do_cal 'procmode' Calibration requirement
Allowed values:
procmode Use the default rules set by the processingmode.
force Require a calibration regardless of theprocessing mode.
skip Skip this correction, no calibration required.
None Field is optional
suffix '_darkCorrected' Filename suffix
dark None Dark frame
The parameter do_cal controls the dark correction. Set to “skip”.
Question 3:
The last question is about the stacking of the reduced frames. At the end
of the recipe there’s the primitive stackFrames.
showpars ../playdata/example1/N20160102S0270.fits stackFrames
...
reject_method 'sigclip' Pixel rejection method
Allowed values:
none no rejection
minmax reject highest and lowest pixels
sigclip reject pixels based on scatter
varclip reject pixels based on variance array
...
The reject_method is the answer. It can be set to minmax, to none,
to varclip, or to sigclip (currently the default).
Solutions to the Explore data exercises
Solution to Exercise - Explore 1
Question 1
reduce -r display N20160102S0271_stack.fits -p frame=2 threshold=None
Note that we did not use the display: prefix, like in display:frame=2.
When the primitive name is not specified, the instruction applies to all
parameters with that name from any primitives in the recipe. Here, we get
away with it because only the display primitive is being run.
Question 2
reduce -r inspect @stdstar.lis -p pause=1
Solutions to the Local calibration manager exercises
Solution to Exercise - Caldb 1
The dragonsrc file should look like this:
[calibs]
#databases = <where_the_data_package_is>/niriimg_tutorial/playground/cal_manager.db get
databases = <where_the_data_package_is>/niriimg_tutorial/cal_manager.db get
ls <where_the_data_package_is>/niriimg_tutorial should show a file called
cal_manager.db. And
caldb config
Using configuration files: ('~/.dragons/dragonsrc',)
/Users/klabrie/data/tutorials/niriimg_tutorial/cal_manager.db
Type: LocalDB
Get: True
Store: False
Database file: /Users/klabrie/data/tutorials/niriimg_tutorial/cal_manager.db
The calibration dbs are all local, meaning that remote calibrations will not be downloaded
Solution to Exercise - Caldb 2
It is important do to successfully complete Exercise - Caldb 1 before attempting Exercise 2.
First confirm that the new calibration manager, the one niriimg_tutorial
is active.
caldb config
Using configuration files: ('~/.dragons/dragonsrc',)
/Users/klabrie/data/tutorials/niriimg_tutorial/cal_manager.db
Type: LocalDB
Get: True
Store: False
Database file: /Users/klabrie/data/tutorials/niriimg_tutorial/cal_manager.db
The calibration dbs are all local, meaning that remote calibrations will not be downloaded
Question 1
caldb add N20160102S0373_flat.fits
Question 2
caldb list
N20160102S0373_flat.fits /data/workspace/niriimg_tutorial/playground
Question 3
Edit dragonsrc. Comment out the niriimg_tutorial path and uncomment
the playground path.
[calibs]
databases = <where_the_data_package_is>/niriimg_tutorial/playground/cal_manager.db get
#databases = <where_the_data_package_is>/niriimg_tutorial/cal_manager.db get
Confirm activation with caldb config.
caldb list
N20160102S0373_flat.fits /data/workspace/niriimg_tutorial/playground
N20160102S0423_dark.fits /data/workspace/niriimg_tutorial/playground
Solutions to the reduce exercises
Solution to Exercise - “reduce” 1
reduce -r detectSources N20160102S0296_stack.fits --suffix _ILoveDRAGONS
Or
reduce -r detectSources N20160102S0296_stack.fits --suffix=_ILoveDRAGONS
Solution to Exercise - “reduce” 2
reduce @flats.lis -p normalizeFlat:scale=mean --suffix _exercise2
Solution to Exercise - “reduce” 3
reduce @target.lis -r makeSkyFlat --suffix _skyflat
Solution to Exercise - “reduce” 4
While it is not recommended to use a processed dark of the wrong exposure, here is how you would force DRAGONS to use the science’s master dark on the flux standard from the demo.
reduce @stdstar.lis -p --user_cal processed_dark:N20160102S0423_dark.fits
Solutions to the Customize recipes exercise
Solution to Exercise - Custom Recipe 1
showrecipes ../playdata/example1/N20160102S0363.fits
cp /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_FLAT_IMAGE.py .
mv recipes_FLAT_IMAGE.py myNIRIflats.py
p.prepare()
p.addDQ()
p.addVAR(read_noise=True)
p.nonlinearityCorrect()
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.makeLampFlat()
p.writeOutputs()
p.normalizeFlat()
p.thresholdFlatfield()
p.storeProcessedFlat()
p.display()
reduce @flats.lis -r myNIRIflats.makeProcessedFlat
Solutions to the Tools exercise
Solution to Exercise - Tools 1
showd -d exposure_time,filter_name,ut_date ../playdata/example1/*.fits
-----------------------------------------------------------------------------------
filename exposure_time filter_name ut_date
-----------------------------------------------------------------------------------
../playdata/example1/N20160102S0270.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0271.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0272.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0273.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0274.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0275.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0276.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0277.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0278.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0279.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0295.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0296.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0297.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0298.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0299.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0363.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0364.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0365.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0366.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0367.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0368.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0369.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0370.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0371.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0372.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0373.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0374.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0375.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0376.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0377.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0378.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0379.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0380.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0381.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0382.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0423.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0424.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0425.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0426.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0427.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0428.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0429.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0430.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0431.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0432.fits 20.002 blank 2016-01-02
../playdata/example1/N20160103S0463.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0464.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0465.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0466.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0467.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0468.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0469.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0470.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0471.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0472.fits 1.001 blank 2016-01-03
Solution to Exercise - Tools 2
dataselect ../playdata/example1/*.fits --expr='observation_class=="science" and observation_type=="OBJECT"' | showd -d object
--------------------------------------------------
filename object
--------------------------------------------------
../playdata/example1/N20160102S0270.fits SN2014J
../playdata/example1/N20160102S0271.fits SN2014J
../playdata/example1/N20160102S0272.fits SN2014J
../playdata/example1/N20160102S0273.fits SN2014J
../playdata/example1/N20160102S0274.fits SN2014J
../playdata/example1/N20160102S0275.fits SN2014J
../playdata/example1/N20160102S0276.fits SN2014J
../playdata/example1/N20160102S0277.fits SN2014J
../playdata/example1/N20160102S0278.fits SN2014J
../playdata/example1/N20160102S0279.fits SN2014J
dataselect ../playdata/example1/*.fits --expr='observation_class=="partnerCal" and observation_type=="OBJECT"' | showd -d object
-------------------------------------------------
filename object
-------------------------------------------------
../playdata/example1/N20160102S0295.fits FS 17
../playdata/example1/N20160102S0296.fits FS 17
../playdata/example1/N20160102S0297.fits FS 17
../playdata/example1/N20160102S0298.fits FS 17
../playdata/example1/N20160102S0299.fits FS 17
Basic DRAGONS Workshop - Short Version
This is the material for truncated version of the Basic DRAGONS Workshop. The full workshop requires 2 hours. This shorter version is better adapted for a one-hour time slot.
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
reduce.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.
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, Installation Guide.
Follow the Python 3 and DRAGONS instructions, IRAF is not needed. Make sure that you do the steps in the “Configure DRAGONS” section.
Important
This workshop is written for DRAGONS v3.1
The participants will need to download the data package to run the examples and the exercises:
http://www.gemini.edu/sciops/data/software/datapkgs/niriim_tutorial_datapkg-extended-v1.tar
Download it and unpack it somewhere convenient.
cd <somewhere convenient>
tar xvf niriim_tutorial_datapkg-extended-v1.tar
bunzip2 niriimg_tutorial/playdata/example1/*.bz2
The datasets are found in the subdirectory niriimg_tutorial/playdata/example1, and
we will work in the subdirectory named niriimg_tutorial/playground.
Finally, for this short version, additional files that would normally be
created during the full workshop but are not due to the time constraint
will need to be download and added to the niriimg_tutorial/playground
directory.
https://drive.google.com/file/d/1sJd8bHUFZ3-dNVT3_VllcqplnoSZr4LJ/view?usp=sharing
cd <somewhere convenient>/niriimg_tutorial/playground
tar xvzf demo_outputs.tar.gz
The content of the niriimg_tutorial/playground directory should look like
this:
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
What is DRAGONS
What it is
With a little bit of guidance from you, DRAGONS will reduce your Gemini data in a standard way, rapidly, with little fuss. With a bit more guidance from you, DRAGONS will let you optimize the reduction for your specific sources and science objectives.
DRAGONS is operated through the reduce command from the shell, or the
Reduce class from a Python script. You choose. For clarity, we will refer
to reduce only from now on, but it’s the same thing. A call to reduce
activates the “Recipe System” which is what automates everything.
When reduce is called, the first FITS file is opened and identified, then
the libraries of algorithms collected as “primitives” and “recipes” will be
searched and the best matches will be selected and run on the data.
An important component of the DRAGONS automation infrastructure is the
calibration manager. It is operated with the caldb command. (Again,
there’s an API too.) This accesses a lightweight local database that
will store information about your locally-processed calibrations. When a
primitive is run and needs a calibration, the system will automatically
identify the best matched processed calibration and use it, you do not have to
specify it on the command line. (Though, you can if you really want to.)
Then there’s a series of utilities for sorting through the data and learning about the primitives and the recipes.
All of it uses astrodata which is used to open the FITS files. Once a file
is opened with astrodata, it “knows about itself” and that’s how it can
find the recipes, primitives, and calibration it needs.
Here’s what DRAGONS can look like from a user’s point view:
dataselect *.fits --tags=FLAT -o flat.lis
dataselect *.fits --expr="object='mycooltarget'" -o sci.lis
caldb init
reduce @flat.lis
caldb add *_flat.fits
reduce @sci.lis
The steps are:
Create your lists of input data.
Initialize your calibration database.
Reduce your calibrations and upload the info to the database (the upload can be automated).
Reduce your science, it will pick up the calibrations by itself.
As straightforward as a reduction can be, it can be customized to match your needs. The options to the primitives can be adjusted, the recipes themselves can be adjusted.
In this basic introduction to DRAGONS, we will explore all this and learn how to have DRAGONS do our bidding.
What it is not
DRAGONS is not a data analysis package. DRAGONS prepares the data for analysis but does not offer tools to do the analysis. Also, DRAGONS is also not a replacement for IRAF.
To inspect your data, to do any sort of analysis like photometry, measuring redshift, calculating equivalent width, etc, you will have to find another tool. DRAGONS does provide a primitive to display an image to ds9, but it will just display it. Similarly, there is a tool to plot a 1D spectrum, and again, it will just plot it, it not offer any measurement capabilities.
Any analysis-type tools are beyond the scope of DRAGONS.
Components in action
The diagram below illustrates how the components communicate with each other
and in particular how the astrodata tags and descriptors are passed around
to make decisions about the best matched recipe, primitive sets, and
calibrations.
What are Recipes, Recipe Libraries, and Primitives
A DRAGONS recipe is a set of instructions, called primitives, that processes data in a certain way. Depending on the recipe, the input data can be in any state of processing. Most often though, people will want to process Gemini raw data into a master calibration or a processed calibrated output.
A recipe library is a collection of recipes. The recipes in a library have one thing in common: they all apply to the same type of data. When DRAGONS search for a matching recipe for some input data, it searches for a recipe library. In each library, one recipe is set as the default recipe. To use the others, the user needs to specify the name of the non-default recipes.
A primitive is the function that does something to the data. The name of the primitive normally gives you a good idea of what its purpose is.
Recipes
This is what a recipe looks like:
def reduce(p):
p.prepare()
p.addDQ()
p.removeFirstFrame()
p.ADUToElectrons()
p.addVAR(read_noise=True, poisson_noise=True)
p.nonlinearityCorrect()
p.darkCorrect()
p.flatCorrect()
p.separateSky()
p.associateSky(stream='sky')
p.skyCorrect(instream='sky', mask_objects=False, outstream='skysub')
p.detectSources(stream='skysub')
p.transferAttribute(stream='sky', source='skysub', attribute='OBJMASK')
p.clearStream(stream='skysub')
p.associateSky()
p.skyCorrect(mask_objects=True)
p.detectSources()
p.adjustWCSToReference()
p.resampleToCommonFrame()
p.stackFrames()
p.writeOutputs()
A recipe is a Python function that calls “primitives” sequentially. The user does not need to know Python to understand more or less what is being done to the data when the recipe is run. From the recipe above, we can read that the data will be corrected for dark current, for flat field effects, the sky background will be subtracted and the frames will be stacked. The p argument is the primitive set that matches the input.
Recipe libraries
Recipes are stored in recipe libraries, or in Python language, a module. A recipe library can have one or more recipes in them. One recipe is identified as the default recipe.
When DRAGONS searches for a recipe, it is actually searching for a matching recipe library. Once found, it will run the default recipe, unless instructed otherwise by the user.
The recipe libraries are associated with the input files by matching
astrodata tags (Astrodata User Manual). The tags are qualifiers like “NIRI”,
“IMAGE”, “FLAT”. The tags of the
first file in the list of inputs are used. Each recipe library is assigned a
set of tags that defines which type of data this library is for.
Primitives and primitive sets
A primitive is a data reduction step involving a transformation of the data or
providing a service. By convention, the primitives are named to convey the
scientific meaning of the transformation. For example biasCorrect will
remove the bias signal from the input data.
A primitive is always a member of a primitive set. It is the primitive set that gets matched to the data by the Recipe System, not the individual primitives.
Technically, a primitive is a method of a primitive class. A primitive class
gets associated with the input dataset by matching the astrodata tags. Once
associated, all the primitives in that class, locally defined or inherited,
are available to reduce that dataset. We refer to that collection of
primitives as a “primitive set”.
Primitive input parameters
Also attached to a primitive set are the input parameters for each primitives and the defaults appropriate for that primitive set, that is for that type of data.
For the same generic primitive, the default values for input parameters for NIRI data can be different from the defaults applicable to GMOS data. Even the set of available input parameters can be different, though we try to keep things as uniform as possible.
Exploring recipes and primitives
showrecipes
showrecipes allows the user to see which recipe library gets picked up by the system and which recipe is run by default, which others are available.
The syntax is:
showrecipes fitsfile_name.fits
Let’s look at a couple examples. From the niriimg_tutorial/playdata/example1/
directory:
Recipe not provided, default recipe (makeProcessedFlat) will be used.
Input file: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata/example1/N20160102S0373.fits
Input tags: ['GCALFLAT', 'NORTH', 'AT_ZENITH', 'NON_SIDEREAL', 'AZEL_TARGET', 'RAW', 'IMAGE', 'GCAL_IR_ON', 'NIRI', 'GEMINI', 'UNPREPARED', 'LAMPON', 'CAL', 'FLAT']
Input mode: sq
Input recipe: makeProcessedFlat
Matched recipe: geminidr.niri.recipes.sq.recipes_FLAT_IMAGE::makeProcessedFlat
Recipe location: /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_FLAT_IMAGE.py
Recipe tags: {'FLAT', 'IMAGE', 'CAL', 'NIRI'}
Primitives used:
p.prepare()
p.addDQ()
p.addVAR(read_noise=True)
p.nonlinearityCorrect()
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.makeLampFlat()
p.normalizeFlat()
p.thresholdFlatfield()
p.storeProcessedFlat()
This contains: the name of the recipe, the location of the recipe library, the :astrodata: tags of the input file, those assigned to the recipe library, and the recipe itself.
A library can have more than one recipe, to see them all use the --all
flag:
showrecipes N20160102S0270.fits --all
Input file: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata/example1/N20160102S0270.fits
Input tags: {'NIRI', 'NORTH', 'GEMINI', 'UNPREPARED', 'IMAGE', 'RAW', 'SIDEREAL'}
Recipes available for the input file:
geminidr.niri.recipes.sq.recipes_IMAGE::alignAndStack
geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.sq.recipes_IMAGE::reduce
geminidr.niri.recipes.qa.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.qa.recipes_IMAGE::reduce
geminidr.niri.recipes.sq.recipes_IMAGE::alignAndStack
geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.sq.recipes_IMAGE::reduce
The recipe library for science quality has three recipes: alignAndStack,
makeSkyFlat, and reduce.
Note
Regarding the “sq” and “qa” in the paths. DRAGONS has the concept of “reduction mode”. Right now, there are two modes: the science quality mode, “sq”, and the quality assessment mode, “qa”. You can safely ignore the “qa” mode, it is used exclusively at the observatory, at night, to help with the assessment of the sky conditions and the resulting quality of the data. Everything defaults to “sq”.
The last three “sq” recipes are really the “ql” recipes. This a known
bug (circa April 2023). The NIRI quicklook recipes are identical to
the science recipes and are just “Python imported” from the science module,
and that import trips the current implementation of showrecipes.
To see what a specific recipe looks like, not just the default recipe, use
the -r flag:
showrecipes N20160102S0270.fits -r makeSkyFlat
Input file: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata/example1/N20160102S0270.fits
Input tags: ['UNPREPARED', 'NORTH', 'NIRI', 'SIDEREAL', 'RAW', 'IMAGE', 'GEMINI']
Input mode: sq
Input recipe: makeSkyFlat
Matched recipe: geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
Recipe location: /Users/klabrie/condaenvs/gemini3_2.1.x_20200925/lib/python3.6/site-packages/dragons-2.1.1-py3.6-macosx-10.7-x86_64.egg/geminidr/niri/recipes/sq/recipes_IMAGE.py
Recipe tags: {'IMAGE', 'NIRI'}
Primitives used:
p.prepare()
p.addDQ()
p.ADUToElectrons()
p.addVAR(read_noise=True, poisson_noise=True)
p.nonlinearityCorrect()
p.darkCorrect()
p.stackFrames(operation='median', scale=True, outstream='fastsky')
p.normalizeFlat(stream='fastsky')
p.thresholdFlatfield(stream='fastsky')
p.flatCorrect(flat=p.streams['fastsky'][0], outstream='flattened')
p.detectSources(stream='flattened')
p.dilateObjectMask(dilation=10, stream='flattened')
p.addObjectMaskToDQ(stream='flattened')
p.writeOutputs(stream='flattened')
p.transferAttribute(source='flattened', attribute='mask')
p.stackFrames(operation='mean', scale=True, reject_method="minmax", nlow=0, nhigh=1)
p.normalizeFlat()
p.thresholdFlatfield()
p.storeProcessedFlat(force=True)
showpars
Primitive input parameters can be customized. To see the available input parameters, their default values, and range of allowed values, use showpars. The syntax is:
showpars filename.fits primitive_name
A dataset is required here because the parameters and their defaults do change depending on the type of data. Defaults between instruments can be different because the data has different characteristics. Also, the primitive might be doing slightly different things (eg. optical vs near-IR).
Let’s say that we want to reduce NIRI science data, an example of which is
file N20160102S0270.fits but we want to customize the sky correction,
the dark correction, and the rejection method during the final stack. Here’s
how one would proceed to check for primitive names and parameters names.
showrecipes N20160102S0270.fits
...
p.prepare()
p.addDQ()
p.removeFirstFrame()
p.ADUToElectrons()
p.addVAR(read_noise=True, poisson_noise=True)
p.nonlinearityCorrect()
p.darkCorrect()
p.flatCorrect()
p.separateSky()
p.associateSky(stream='sky')
p.skyCorrect(instream='sky', mask_objects=False, outstream='skysub')
p.detectSources(stream='skysub')
p.transferAttribute(stream='sky', source='skysub', attribute='OBJMASK')
p.clearStream(stream='skysub')
p.associateSky()
p.skyCorrect(mask_objects=True)
p.detectSources()
p.adjustWCSToReference()
p.resampleToCommonFrame()
p.stackFrames()
p.writeOutputs()
Here I spot the primtives: skyCorrect, darkCorrect, and stackFrames.
If I wanted to turn off the dark correction…
showpars N20160102S0270.fits darkCorrect
Dataset tagged as {'NORTH', 'IMAGE', 'NIRI', 'UNPREPARED', 'SIDEREAL', 'GEMINI', 'RAW'}
Settable parameters on 'darkCorrect':
========================================
Name Current setting
do_cal 'procmode' Calibration requirement
Allowed values:
procmode Use the default rules set by the processingmode.
force Require a calibration regardless of theprocessing mode.
skip Skip this correction, no calibration required.
None Field is optional
suffix '_darkCorrected' Filename suffix
dark None Dark frame
I would set do_cal to “skip”.
If I wanted to change the operation done when combining sky frames to
a mean…
showpars N20160102S0270.fits skyCorrect
...
operation 'median' Averaging operation
Allowed values:
mean arithmetic mean
wtmean variance-weighted mean
median median
lmedian low-median
...
I would reset operation to mean.
If I wanted to change the rejection method when doing the final stack…
showpars N20160102S0270.fits stackFrames
...
reject_method 'sigclip' Pixel rejection method
Allowed values:
none no rejection
minmax reject highest and lowest pixels
sigclip reject pixels based on scatter
varclip reject pixels based on variance array
...
I would pick one of the allowed value for reject_method.
Demo imaging
To get ourselves oriented and get a feel for how data is processed with DRAGONS, we will run a full reduction on our sample data. The full tutorial to reduce NIRI data can be found at NIRI Imaging Data Reduction Tutorial.
Here, we go through the steps with quite a bit less discussion than in the full tutorial as the purpose is simply to provide some context and an idea of a typical data reduction flow to those who have not experience it before.
The observations are of an extended source, a nearby galaxy. The instrument used is NIRI. The science sequence is a series of dithers on target with full offset to sky. We will create a master dark, a master flat, a reduced flux standard, and finally create a calibrated, sky-subtracted stack of the science observations.
Let’s run all the steps.
Set up the local calibration manager
Create the file lists
Create the calibration files (dark, flat)
Reduce the flux standard
Reduce the science observations
cd <where_the_data_package_is>/niriimg_tutorial/playground
Set up the local calibration manager
We will discuss the local calibration manager in a later chapter.
The calibration manager is the first thing to set up when starting on a data reduction project. It provides the automated calibration association.
The file to pay attention to is: ~/.dragons/dragonsrc (DRAGONS v3.1+)
For this example, that file to contain the following:
[calibs]
databases = <where_the_data_package_is>/niriimg_tutorial/playground/cal_manager.db get
In this configuration, with the “get” flag, DRAGONS will retrieve the processed
calibration automatically from that database. You could add “store” after the
“get” to have DRAGONS add the processed calibrations to the
database automatically (effectively running caldb add for you). Here we
will add the calibrations it manually.
About the path for databases, we recommend the directory you are using
to work on the data. But it can be anywhere.
Once the configuration file is set up, initialize the database. This needs to be done only once per database.
caldb init
This creates a new file, cal_manager.db, in the directory specified by
databases.
Create the file lists
The user has to sort the files. DRAGONS is a “User-in-the-Loop” pipeline. We have some tools to help. We will have a closer look at them later.
We have long darks that match the science, a series of lamp-on and lamp-off flats, a set of on-target dithered observations for a flux standard, and a set of on-target dithered and off-target dithered observations for the science target.
dataselect ../playdata/example1/*.fits --tags DARK --expr='exposure_time==20' -o darks20s.lis
dataselect ../playdata/example1/*.fits --tags FLAT -o flats.lis
dataselect ../playdata/example1/*.fits --expr='object=="FS 17"' -o stdstar.lis
dataselect ../playdata/example1/*.fits --expr='object=="SN2014J"' -o target.lis
Add static BPM to calibration database
The static BPMs are downloaded from the archive (but included here in our data package).
caldb add ../playdata/example1/bpm_20010317_niri_niri_11_full_1amp.fits
Create the master dark and the master flat
Dark
Create the master dark and add it to the calibration manager.
reduce @darks20s.lis
caldb add N20160102S0423_dark.fits
The @ character before the name of the input file is the “at-file” syntax.
We will look into this later.
Flat
Create the master flat from the lamp-on and lamp-off flats and add it to the calibration database.
reduce @flats.lis
caldb add N20160102S0373_flat.fits
Reduce the flux standard
This is short series of on-target dither observations. Darks are not obtained for NIRI flux standard so we turn that step off. The flat will be picked up from the calibration database.
reduce @stdstar.lis -p darkCorrect:do_cal=skip
Reduce the science observations
The sequence is a set of dither on target with full offsets to sky. DRAGONS will sort them during the sky correction. All calibrations are picked up automatically from the calibration database. Because the target fills the field of view, we need to turn off the scaling of the sky frames.
reduce @target.lis -p skyCorrect:scale_sky=False
Explore data
Once we have reduced data, we probably want to look at. In fact, even before we start the reduction, we should look at the raw data in case of issues.
DRAGONS is not an analysis package. But it does offer a basic way to display images.
The display and inspect primitives can be used to visually inspect
the data with ds9. Those are primitives not recipes, yet reduce can
run individual primitives.
Note
ds9 must be launched by the user ahead of running the primitive.
Display
To display an image to ds9:
reduce -r display N20160102S0271_stack.fits
It is possible to display to different buffer in ds9, eg. to allow
blinking between the frames.
reduce -r display N20160102S0271_stack.fits -p frame=2
Inspect
It is recommended to inspect the raw data prior to reducing the data. A quick
visual inspection can catch obviously bad data, or flag some frames for
more detailed inspection. Having to run display on each frame individually
would be painful. The primitive inspect can be used here. It
accepts a list of file and will display each frame in sequence, adding a short
pause between the frames. The length of the pause can be controlled with
the pause parameter.
reduce -r inspect @stdstar.lis
reduce -r inspect @stdstar.lis -p pause=1
Local calibration manager
The local calibration manager is the tool that will allow DRAGONS to find the most appropriate processed calibration for the data being reduced.
The local calibration manager contains all the same rules as the Gemini
Observatory Archive, but works locally, with your files, without internet, and
alongside DRAGONS to make the calibration association as seamless as possible.
As we have seen in the demo earlier, we create the master calibrations, we add
them to the database, and when DRAGONS (reduce) needs one, it gets matched
and retrieved automatically.
The system uses a lightweight sqlite database that will not store the
data file, it will store only information about the file, like its
location on disk, and key astrodata tags and descriptors.
Warning
If you move or delete a processed calibration on disk after having entered it in the database, the calibration manager will no longer be able to find it. The database does not contain the file, just information about the file.
See the caldb documentation for more information, including information about the programming interface.
Configuration and initialization
Warning
The information provided here applies to DRAGONS version 3.1 (and newer). The configuration and features of the calibration manager in the previous versions, 3.0 and older, are substantially different. Please refer to a previous version of this workhop if you are using DRAGONS 3.0 or older,
Configuration
The behavior and configuration of the local calibration manager are controlled
in the file ~/.dragons/dragonsrc. When you first install DRAGONS, you
will have to create that directory and that file.
To complete the exercises, your dragonsrc file must look like this:
[calibs]
databases = <where_the_data_package_is>/niriimg_tutorial/playground/cal_manager.db get
The value of databases can be any existing path on your machine. The
calibration database will be created, and then expected to be in that directory.
The database name can be anything, here we name it cal_manager.db for
continuity with pre-v3.1 DRAGONS.
We recommend setting up a new database for each observing project. It helps keep things clean and avoids calibrations from other programs being unexpectedly picked up. (If they were picked up, they would be a match, but it might get confusing if you are expecting another calibration to be picked up.)
Here is an example of a dragonsrc with several databases entries.
Note that at all times, only one databases directive is active, the
others are commented out.
[calibs]
databases = /Users/klabrie/data/tutorials/niriimg_tutorial/playground/cal_manager.db get
#databases = /Users/klabrie/data/tutorials/gmosimg_tutorial/playground/cal_manager.db get
#databases = /Users/klabrie/data/workspace/GMOS540feature/cal_manager.db get
Initialization
Once your dragonsrc is set up, you can create a new database with
caldb init
If the database already exists, that command will refuse to work. This ensures
that you will not wipe the database accidentally. The init is required
only once. If you already have the database created, maybe even populated,
you can switch back to it simply by ensuring databases points to it.
Exercise - Caldb 1
Create a new database in the niri_tutorial directory. Keep the
playground entry that should already be there, but deactivate it.
Confirm with ls that a new cal_manager.db has been created. You
can also use caldb config to confirm that the new database is active.
[Solution]
Usage
The usage is fairly basic. One can list the content, add content,
and remove content. DRAGONS will retrieve content from the database. If
the “store” flag is set, DRAGONS will also automatically add content to the
database.
Warning
In DRAGONS version 3.0 and older, DRAGONS cannot add content
automatically, the user must add the processed calibrations to the
database with an explicit caldb add.
To verify which database is currently active, use the config option:
caldb config
To see the content of the database:
caldb list
To add information about a processed calibration to the database:
caldb add name_of_file.fits
And finally, to delete information about one file from the database:
caldb remove name_of_file.fits
remove will only remove the entry in the database, it will not remove
the file on disk.
Exercise - Caldb 2
Do Exercise - Caldb 1 first.
Add the flat field from the demo (
N20160102S0373_flat.fits) to the new calibration manager created in the first exercise.Show the content of that database is indeed just that file.
Reactivate the original database (the one in
playgroundthat we used for the demo) and list its content. Both the dark and the flat should now be listed.
[Solution]
Using reduce
The reduce command (and the Reduce class if you are using the
programming interface) is what drives DRAGONS. The command has a lot of
options. We have used a few already. Let’s review those and show a few more.
First, to see the list of options available, use the -h flag:
reduce -h
Setting the suffix of the final outputs
It is not possible to set the name of the final output. This seems odd, but here is why. There might be more than one output, there can be dozens, it depends on the recipe. It is not reasonable to ask the user for a dozen different output strings on the command line, it also interferes with the automation.
We are still exploring solutions to this problem.
In the meantime, the user can set the suffix of the outputs. Each primitive
has a meaningful suffix attached to it, for example _sourcesDetected for
the primitive detectSources. The output of a reduce call will get
the suffix assigned to the last primitive of the recipe.
If you wish to override that, you can set the --suffix option.
reduce @targets --suffix=_mytest
Customizing parameters
We have seen earlier how to check the available input parameters and their default settings for a given frame and primitive. The command showpars does that. For example:
showpars ../playdata/example1/N20160102S0363.fits normalizeFlat
The syntax to change the value of an input parameter is as follow:
reduce file.fits -p primitive:parameter=value
Exercise - “reduce” 1
White down the reduce command to reduce the flats from the demo
(flats.lis) but this time set the scaling for normalizeFlat flat
to mean. To avoid overwriting our “real” processed
flat, let’s set the suffix to _exercise1.
Hint #1: cat flats.lis to get the name of a flat to use with showpars.
Hint #2: See the command used in the demo
[Solution]
Running non-default recipes
A recipe library often contains several recipes. One recipe in the library
is set as the default library. If one wants to run a different library,
its name can be specified on the command line using the -r flag.
As we have seen before to see the list of all available recipes, we can use the
showrecipes command with the --all flag.
showrecipes ../playdata/example1/N20160102S0270.fits --all
Input file: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata/example1/N20160102S0270.fits
Input tags: {'GEMINI', 'NORTH', 'NIRI', 'UNPREPARED', 'SIDEREAL', 'IMAGE', 'RAW'}
Recipes available for the input file:
geminidr.niri.recipes.sq.recipes_IMAGE::alignAndStack
geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.sq.recipes_IMAGE::reduce
geminidr.niri.recipes.qa.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.qa.recipes_IMAGE::reduce
geminidr.niri.recipes.sq.recipes_IMAGE::alignAndStack
geminidr.niri.recipes.sq.recipes_IMAGE::makeSkyFlat
geminidr.niri.recipes.sq.recipes_IMAGE::reduce
The strings sq and qa refer to the reduction mode. The qa mode,
Quality Assessment, is used internally at Gemini. General users will be
using the sq, Science Quality, recipes.
Warning
The last three “sq” recipes
in the list above are really the “ql”, Quicklook, recipes. This a known
bug (circa April 2023). The NIRI quicklook recipes are identical to
the science recipes and are just “Python-imported” from the science module,
and that import trips the current implementation of showrecipes.
The reduction mode is sq by default. To change that, one uses the --qa
or soon --ql flags with reduce. We will be using the science quality
recipes here so we do not need those flags in this version of workshop.
The syntax to select the specific recipe we want to use is:
reduce file1.fits file2.fits -r recipename (optional --ql/--qa)
The -r option can also be used to primitives as we have seen elsewhere.
Just type the name of the primitive instead of the recipe:
reduce file1.fits file2.fits -r primitivename
Another use of the -r option is to run personal recipes rather than the
ones distributed with DRAGONS. We will show how that is done in the next
chapter.
Exercise - “reduce” 2
Write the command to use the makeSkyFlat recipe on the science frames.
Set the suffix to _skyflat.
Note that because
the target fills the field-of-view of the science frames, the sky flat
in this particular case will not be usable, but it’s okay, we are just
exploring the reduce command-line here.
[Solution]
Overriding the calibration selection
If you wish to force DRAGONS to use a specific processed calibration, overriding
the automatic selection, you can use the --user_cal flag. Here is a
usage example.
reduce file1.fits file2.fits --user_cal processed_<cal>:my_cal.fits
Allowed values for “<cal>”: dark, bias, flat, arc, standard. Eg:
reduce @mylist.lis --user_cal processed_arc:my_special_arc.fits
Exercise - “reduce” 3
In the demo, we reduced the flux standard as follow:
reduce @stdstar.lis -p darkCorrect:do_cal=skip
Modify this command to allow the dark correction to use the processed
dark we used for the science frame, N20160102S0423_dark.fits.
[Solution]
Customize Recipes
Sometimes the DRAGONS recipes are not quite what is needed. It is possible to customize a recipe and have DRAGONS run it. The easiest way to customize a recipe is to find the most appropriate one, copy it over and edit it.
Here we will show you how to write intermediate results to disk for inspection.
The first step is to find a recipe and copy it to the local directory. Let’s find the default NIRI science imaging recipe.
showrecipes ../playdata/example1/N20160102S0270.fits
The recipe name and the recipe library location returned will look like this:
...
Input recipe: reduce
...
Recipe location: /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_IMAGE.py
...
Note
The file might end with .pyc instead of .py. This is the
compiled file, the code file is in the same location and named .py. That
is the file we will copy over. It is not clear at this time why the .pyc
is sometimes returned instead of the .py filename.
cp /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_IMAGE.py .
You should now have a file named recipes_IMAGE.py in your current directory.
Let’s rename it for clarity:
mv recipes_IMAGE.py myNIRIrecipes.py
Use your favorite editor to open myNIRIrecipes.py to edit the recipe named
reduce. We will save some intermediate results: the flat corrected files
and the sky corrected frames before alignment and stacking. To do that we
need to add writeOutputs after flatCorrect and skyCorrect.
p.prepare()
p.addDQ()
p.removeFirstFrame()
p.ADUToElectrons()
p.addVAR(read_noise=True, poisson_noise=True)
p.nonlinearityCorrect()
p.darkCorrect()
p.flatCorrect()
p.writeOutputs()
p.separateSky()
p.associateSky(stream='sky')
p.skyCorrect(instream='sky', mask_objects=False, outstream='skysub')
p.detectSources(stream='skysub')
p.transferAttribute(stream='sky', source='skysub', attribute='OBJMASK')
p.clearStream(stream='skysub')
p.associateSky()
p.skyCorrect(mask_objects=True)
p.writeOutputs()
p.detectSources()
p.adjustWCSToReference()
p.resampleToCommonFrame()
p.stackFrames()
p.writeOutputs()
return
Note that some of the primitives in the recipe have parameters set in the recipe itself. Parameter values set in a recipe will always take precedence. Those cannot be changed by the user from the command line.
Now that we have an edited recipe that will write the flat corrected and the
pre-alignment sky corrected frames, how do we run it? With -r of course!
But the syntax is just a touch different. We need to specific both the
name of our recipe library (myNIRIrecipes) and the name of the recipe
(reduce).
reduce @target.lis -r myNIRIrecipes.reduce
The new intermediate outputs have the suffixes _flatCorrected and
_skySubtracted.
Exercise - Custom Recipe 1
Edit the master flat recipe to write the pre-normalized flat to disk, and to display the master flat at the end of the recipe.
Hint: cat flats.lis to get the name of a flat to use with showrecipes
to get the location and name of the NIRI flat recipe library.
[Solution]
Tools
DRAGONS offers some tools to help with the bookkeeping. We have used
dataselect already to create lists, we used showpars and showrecipes
to check the primitive parameter settings and the recipes.
Here we will explore dataselect a bit more, and introduce showd and
typewalk.
Additional information about the tools can be found in the Supplemental Tools chapter of the Recipe System Users Manual.
typewalk
The oddly-named typewalk tool allows the user the list the astrodata tags
(formerly “types”) of all the files in a directory, and can recurse (“walk”)
through subdirectories. It can also be used to select data on tags, but we
recommend using the much more flexible dataselect for that.
To see the type of data files that we have in playdata/example1 and see which
tags are available for selection:
typewalk --dir ../playdata
directory: /Users/klabrie/data/tutorials/niriimg_tutorial/playdata
N20160102S0270.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0271.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0272.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0273.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0274.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0275.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0276.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0277.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0278.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0279.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0295.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0296.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0297.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0298.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0299.fits ............... (GEMINI) (IMAGE) (NIRI) (NORTH) (RAW) (SIDEREAL) (UNPREPARED)
N20160102S0363.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0364.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0365.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0366.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0367.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0368.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0369.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0370.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0371.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0372.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_OFF) (GEMINI) (IMAGE) (LAMPOFF) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0373.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0374.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0375.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0376.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0377.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0378.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0379.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0380.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0381.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0382.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (FLAT) (GCALFLAT) (GCAL_IR_ON) (GEMINI) (IMAGE) (LAMPON) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0423.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0424.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0425.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0426.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0427.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0428.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0429.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0430.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0431.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160102S0432.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0463.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0464.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0465.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0466.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0467.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0468.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0469.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0470.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0471.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
N20160103S0472.fits ............... (AT_ZENITH) (AZEL_TARGET) (CAL) (DARK) (GEMINI) (NIRI) (NON_SIDEREAL) (NORTH) (RAW) (UNPREPARED)
From that output we see the darks with their DARK tag, the flats with the
FLAT tags, some with LAMPON, some with LAMPOFF. The science frames
do not have the CAL (for calibration) tag.
showd
typewalk shows tags, showd shows the values of astrodata descriptors.
The list available descriptor is included in an appendix of the Astrodata User Manual.
The syntax is:
showd -d descriptor_name filenames
showd -d descriptor1,descriptor2,descriptorN filenames
The default is to print on the terminal. A comma-separate list can be
produced with the --csv flag.
Exercise - Tools 1
Get the exposure time, filter name, and UT date of all the
observations in playdata/example1. Use the descriptors list from the
Astrodata User Manual.
[Solution]
dataselect
dataselect is a powerful tool to create list of exactly the files you want
based on tags and descriptors values.
You can include tags, exclude tags, and use Python comparison operators on the descriptors.
Here are a few examples.
Select only darks:
dataselect ../playdata/example1/*.fits --tags DARK
Select only darks and send the results to a file:
dataselect ../playdata/example1/*.fits --tags DARK -o darks.lis
cat darks.lis
# Includes tags: ['DARK']
# Excludes tags: []
# Descriptor expression: None
../playdata/example1/N20160102S0423.fits
../playdata/example1/N20160102S0424.fits
...
../playdata/example1/N20160103S0471.fits
../playdata/example1/N20160103S0472.fits
Select only non-darks:
dataselect ../playdata/example1/*.fits --xtags DARK
Select lamp-on flats:
dataselect ../playdata/example1/*.fits --tags FLAT,LAMPON
Select any frames with exposure time of 10 seconds:
dataselect ../playdata/example1/*.fits --expr="exposure_time==10"
Select 20-second darks:
dataselect ../playdata/example1/*.fits --tags DARK --expr="exposure_time==20"
Select any frames observed on or after 2016-01-03:
dataselect ../playdata/example1/*.fits --expr="ut_date>='2016-01-03'"
We can combine dataselect and showd. Here we are going to get the
observation type, as defined in the Gemini Observing Tool (OT), for those
frames observed on or after 2016-01-03:
dataselect ../playdata/example1/*.fits --expr="ut_date>='2016-01-03'" | showd -d observation_type
The expression in --expr is a Python expression using the Python syntax.
Note the double == for equality in the examples above. Using a single =
is a common mistake.
Also, be careful with the quotes. The external quotes must be different from the internal quotes used around strings.
Finally, the expression can use the and and or logical operators.
Exercise - Tools 2
Knowing that the science frames and the flux standard have an observation
type of ‘OBJECT’, and that the science frames observation class is
“science” while the flux standards are “partnerCal”, create a list of
science frames and a list of flux standards without using the object
names. Send the output to showd to print the object name.
Descriptors: observation_type, observation_class, object.
[Solution]
Resources
The DRAGONS documentation is available on “readthedocs” at https://dragons.readthedocs.io/
DRAGONS and all its dependencies are installed with conda. Installation instruction and a lot of information about how to use DRAGONS are provided in the Recipe System Users Manual.
The list of descriptors is found in Astrodata User Manual.
There are a series of tutorials available on the main DRAGONS portal. They cover reduction of imaging data for each of the current facility imagers.
The code is available on github at: https://github.com/GeminiDRSoftware/DRAGONS
Request for help should be done via the Gemini Helpdesk system using the Topic “DRAGONS”.
Solutions to exercises
Solutions to the Local calibration manager exercises
Solution to Exercise - Caldb 1
The dragonrc file should look like this:
[calibs]
#databases = <where_the_data_package_is>/niriimg_tutorial/playground/cal_manager.db get
databases = <where_the_data_package_is>/niriimg_tutorial/cal_manager.db get
ls <where_the_data_package_is>/niriimg_tutorial should show a file called
cal_manager.db. And
caldb config
Using configuration files: ('~/.dragons/dragonsrc',)
/Users/klabrie/data/tutorials/niriimg_tutorial/cal_manager.db
Type: LocalDB
Get: True
Store: False
Database file: /Users/klabrie/data/tutorials/niriimg_tutorial/cal_manager.db
The calibration dbs are all local, meaning that remote calibrations will not be downloaded
Solution to Exercise - Caldb 2
It is important do to successfully complete Exercise - Caldb 1 before attempting Exercise 2.
First confirm that the new calibration manager, the one niriimg_tutorial
is active.
caldb config
Using configuration files: ('~/.dragons/dragonsrc',)
/Users/klabrie/data/tutorials/niriimg_tutorial/cal_manager.db
Type: LocalDB
Get: True
Store: False
Database file: /Users/klabrie/data/tutorials/niriimg_tutorial/cal_manager.db
The calibration dbs are all local, meaning that remote calibrations will not be downloaded
Question 1
caldb add N20160102S0373_flat.fits
Question 2
caldb list
N20160102S0373_flat.fits /data/workspace/niriimg_tutorial/playground
Question 3
Edit dragonsrc. Comment out the niriimg_tutorial path and uncomment
the playground path.
[calibs]
databases = <where_the_data_package_is>/niriimg_tutorial/playground/cal_manager.db get
#databases = <where_the_data_package_is>/niriimg_tutorial/cal_manager.db get
Confirm activation with caldb config.
caldb list
N20160102S0373_flat.fits /data/workspace/niriimg_tutorial/playground
N20160102S0423_dark.fits /data/workspace/niriimg_tutorial/playground
Solutions to the reduce exercises
Solution to Exercise - “reduce” 1
reduce @flats.lis -p normalizeFlat:scale=mean --suffix _exercise1
Solution to Exercise - “reduce” 2
reduce @target.lis -r makeSkyFlat --suffix _skyflat
Solution to Exercise - “reduce” 3
While it is not recommended to use a processed dark of the wrong exposure, here is how you would force DRAGONS to use the science’s master dark on the flux standard from the demo.
reduce @stdstar.lis --user_cal processed_dark:N20160102S0423_dark.fits
Solutions to the Customize recipes exercise
Solution to Exercise - Custom Recipe 1
showrecipes ../playdata/example1/N20160102S0363.fits
cp /Users/klabrie/condaenvs/public3.10_3.1.0/lib/python3.10/site-packages/geminidr/niri/recipes/sq/recipes_FLAT_IMAGE.py .
mv recipes_FLAT_IMAGE.py myNIRIflats.py
def makeProcessedFlat(p):
p.prepare()
p.addDQ()
p.addVAR(read_noise=True)
p.nonlinearityCorrect()
p.ADUToElectrons()
p.addVAR(poisson_noise=True)
p.makeLampFlat()
p.writeOutputs()
p.normalizeFlat()
p.thresholdFlatfield()
p.storeProcessedFlat()
p.display()
reduce @flats.lis -r myNIRIflats.makeProcessedFlat
Solutions to the Tools exercise
Solution to Exercise - Tools 1
showd -d exposure_time,filter_name,ut_date ../playdata/example1/*.fits
-----------------------------------------------------------------------------------
filename exposure_time filter_name ut_date
-----------------------------------------------------------------------------------
../playdata/example1/N20160102S0270.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0271.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0272.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0273.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0274.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0275.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0276.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0277.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0278.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0279.fits 20.002 H_G0203 2016-01-02
../playdata/example1/N20160102S0295.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0296.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0297.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0298.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0299.fits 10.005 H_G0203 2016-01-02
../playdata/example1/N20160102S0363.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0364.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0365.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0366.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0367.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0368.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0369.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0370.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0371.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0372.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0373.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0374.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0375.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0376.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0377.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0378.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0379.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0380.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0381.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0382.fits 42.001 H_G0203 2016-01-02
../playdata/example1/N20160102S0423.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0424.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0425.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0426.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0427.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0428.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0429.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0430.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0431.fits 20.002 blank 2016-01-02
../playdata/example1/N20160102S0432.fits 20.002 blank 2016-01-02
../playdata/example1/N20160103S0463.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0464.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0465.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0466.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0467.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0468.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0469.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0470.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0471.fits 1.001 blank 2016-01-03
../playdata/example1/N20160103S0472.fits 1.001 blank 2016-01-03
Solution to Exercise - Tools 2
dataselect ../playdata/example1/*.fits --expr='observation_class=="science" and observation_type=="OBJECT"' | showd -d object
--------------------------------------------------
filename object
--------------------------------------------------
../playdata/example1/N20160102S0270.fits SN2014J
../playdata/example1/N20160102S0271.fits SN2014J
../playdata/example1/N20160102S0272.fits SN2014J
../playdata/example1/N20160102S0273.fits SN2014J
../playdata/example1/N20160102S0274.fits SN2014J
../playdata/example1/N20160102S0275.fits SN2014J
../playdata/example1/N20160102S0276.fits SN2014J
../playdata/example1/N20160102S0277.fits SN2014J
../playdata/example1/N20160102S0278.fits SN2014J
../playdata/example1/N20160102S0279.fits SN2014J
dataselect ../playdata/example1/*.fits --expr='observation_class=="partnerCal" and observation_type=="OBJECT"' | showd -d object
-------------------------------------------------
filename object
-------------------------------------------------
../playdata/example1/N20160102S0295.fits FS 17
../playdata/example1/N20160102S0296.fits FS 17
../playdata/example1/N20160102S0297.fits FS 17
../playdata/example1/N20160102S0298.fits FS 17
../playdata/example1/N20160102S0299.fits FS 17
Basic DRAGONS Workshop - Standard Version (~2hrs)
Basic DRAGONS Workshop - Short Version - Short Version (~1hr)