Getting started tutorial part 4: coadding images¶
In this part of the tutorial series you will combine the individual exposures produced by processCcd.py (from part 2) into deeper coadds (mosaic images). To do this you’ll first define the pixel frame that you’ll mosaic into, called a sky map, and then warp (reproject) images into that sky map. Finally, you will coadd the warped images together into deep images.
Pick up your shell session where you left off in part 2.
That means your current working directory must contain the
DATA directory (the Butler repository).
lsst_distrib package also needs to be set up in your shell environment.
See Setting up installed LSST Science Pipelines for details on doing this.
About sky maps¶
Before you get started, let’s talk about sky maps.
A sky map is a tiling of the celestial sphere, and is used as coordinate system for the final coadded image. A sky map is composed of one or more tracts. Those tracts contain smaller regions called patches. Both tracts and patches overlap their neighbors.
Each tract has a different world coordinate system (WCS), but the WCSs of the patches within a given tract are just linearly-offset versions of the same WCS.
There are two general categories of sky maps:
- Whole sky.
- A selected region containing a set of exposures.
Since this HSC dataset covers a small part of the sky, you’ll make the second type.
Making a sky map¶
Again, you want the sky map to cover exactly the exposures you’ve already processed. The most convenient sky map type for this task is a discrete sky map, which you’ll make with the makeDiscreteSkyMap.py command-line task:
makeDiscreteSkyMap.py DATA --id --rerun processCcdOutputs:coadd --config skyMap.projection="TAN"
As you might guess from the previous commands, the
--id wildcard argument implies that the makeDiscreteSkyMap.py command will consider all exposures in the Butler repository, producing a sky map sized to encompass these images.
The last line of the logging output from makeDiscreteSkyMap.py reads:
makeDiscreteSkyMap INFO: tract 0 has corners (321.161, -0.605), (320.601, -0.605), (320.601, -0.045), (321.161, -0.045) (RA, Dec deg) and 3 x 3 patches
In other words, the sky map you’ve just created has a single tract covering all exposures. That tract is divided into a 3-by-3 grid of patches. When you make coadditions, you’ll make one coaddition per patch, for each filter.
Before we move on, let’s look at two of the other arguments you used with the makeDiscreteSkyMap.py command:
--rerun argument introduces the concept of chaining.
--rerun processCcdOutputs:coadd syntax creates a new rerun called
coadd that’s chained to
processCcdOutputs as an input repository.
This means that you’re writing outputs into the new
coadd rerun without affecting the
Use chained reruns at every data processing phase to get flexibility to try different configurations without modifying the reruns of previous phases.
The Butler follows the full depth of a chain to find a requested dataset.
coadd rerun effectively contains not only the coadd outputs, but also outputs from processCcd.py in the
processCcdOutputs rerun and the original raw data at the root of the repository.
The last thing to notice about the makeDiscreteSkyMap.py command is that you’ve set a task configuration:
You can discover available configurations by running the command with a
--show config argument (similar to the
--show data mode you already saw):
makeDiscreteSkyMap.py DATA --id --rerun processCcdOutputs:coadd --show config
These lines from the output briefly document the
skyMap.projection configuration field:
# one of the FITS WCS projection codes, such as: # - STG: stereographic projection # - MOL: Molleweide's projection # - TAN: tangent-plane projection # config.skyMap.projection='TAN'
Simple configurations of string, int, float, and boolean value types can be made on the command line, like you did here. Some configuration values are Python lists, dictionaries, or even class objects. For these types you’ll need to make a configuration file; you’ll see an example of this later.
Warping images onto the sky map¶
Before assembling the coadded image, you need to warp the exposures created by processCcd.py onto the pixel grids of patches created by makeDiscreteSkyMap.py. You can use the makeCoaddTempExp.py command-line task for this.
The way you select data IDs for warping and coaddition is slightly different than for processing individual exposures because you must select both the exposures to use as inputs and what patches in the sky map to coadd into.
You’ll select exposures to use as inputs with the
This example selects
The output is now specified with the familiar
Instead of an exposure data ID, you’ll specify the coaddition output according to
--id filter=HSC-R tract=0 patch=0,0
patch=0,0 key selects the patch at index
Likewise, the middle patch of the 3-by-3 grid is
Now, you’ll want to make coadditions for all nine patches.
Like you did with processCcd.py, you can supply multiple patches that makeCoaddTempExp.py will iterate over.
To specify multiple patches, you’ll use the
^ (or) operator.
For example, this
--id argument selects both the
--id filter=HSC-R tract=0 patch=0,0^1,0
When you run makeCoaddTempExp.py, you can’t omit the
patch data ID keys as a wild card pattern.
You need to explicity define which patches to make warped exposures for.
Putting this together, run the following command to warp
HSC-R-band exposures into all nine patches:
makeCoaddTempExp.py DATA --rerun coadd \ --selectId filter=HSC-R \ --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \ --config doApplyUberCal=False
makeCoaddTempExp.py automatically filters out exposures that don’t fit on a patch.
Since this tutorial doesn’t prepare an uber calibration, you needed to explicitly disable the uber calibration step that is enabled by default for HSC processing.
Next, repeat the warping step for
makeCoaddTempExp.py DATA --rerun coadd \ --selectId filter=HSC-I \ --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2 \ --config doApplyUberCal=False
Coadding warped images¶
Now you’ll assemble the warped images into coadditions for each patch with the assembleCoadd.py task.
As before, the
--selectId argument selects warped
HSC-R-band exposures while the
--id argument specifies the patches that assembleCoadd.py will make coadds for.
assembleCoadd.py DATA --rerun coadd \ --selectId filter=HSC-R \ --id filter=HSC-R tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2
Run assembleCoadd.py again to make
assembleCoadd.py DATA --rerun coadd \ --selectId filter=HSC-I \ --id filter=HSC-I tract=0 patch=0,0^0,1^0,2^1,0^1,1^1,2^2,0^2,1^2,2
While both the makeCoaddTempExp.py and assembleCoadd.py command-line tasks iterate over patches, they cannot iterate over multiple filters.
That’s why you couldn’t write a
--id filter=HSC-R^HSC-I argument.
In this tutorial, you’ve made a sky map, warped exposures into it, and then coadded the exposures into deep mosaics. Here are some key takeaways:
- Sky maps define the WCS of coadditions.
- Sky maps are composed of tracts, each of which is composed of smaller patches.
- The makeDiscreteSkyMap.py command creates a sky map to encompass a given set of exposures.
- The makeCoaddTempExp.py command warps exposures into the WCSs of the sky map.
- The assembleCoadd.py command coadds warped exposures into deep mosaics for a given patch and filter combination.
--rerun rerunA:rerunBsyntax lets you chain reruns together. Inputs are read from
rerunAand outputs are written to
Continue this tutorial in part 5, where you’ll measure sources in the coadds.