Getting started tutorial part 4: coadding images

In this part of the tutorial series you will combine the individual exposures produced by (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.

Set up

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).

The 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:

  1. Whole sky.
  2. 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 command-line task: DATA --id --rerun processCcdOutputs:coadd --config skyMap.projection="TAN"

As you might guess from the previous commands, the --id wildcard argument implies that the 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 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 command: --rerun and --config.

Rerun chaining

The --rerun argument introduces the concept of chaining. The --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 processCcdOutputs.


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. Thus the coadd rerun effectively contains not only the coadd outputs, but also outputs from in the processCcdOutputs rerun and the original raw data at the root of the repository.

Task configuration

The last thing to notice about the command is that you’ve set a task configuration: --config skyMap.projection="TAN".

You can discover available configurations by running the command with a --show config argument (similar to the --show data mode you already saw): 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

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 onto the pixel grids of patches created by You can use the 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 --selectId argument. This example selects HSC-R-band exposures:

--selectId filter=HSC-R

The output is now specified with the familiar --id argument. Instead of an exposure data ID, you’ll specify the coaddition output according to filter, tract, and patch keys. For example:

--id filter=HSC-R tract=0 patch=0,0

The patch=0,0 key selects the patch at index 0, 0. Likewise, the middle patch of the 3-by-3 grid is 1, 1.

Now, you’ll want to make coadditions for all nine patches. Like you did with, you can supply multiple patches that will iterate over. To specify multiple patches, you’ll use the ^ (or) operator. For example, this --id argument selects both the 0,0 and 1,0 patches:

--id filter=HSC-R tract=0 patch=0,0^1,0


When you run, you can’t omit the tract and 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: 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

Tip 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 HSC-I-band images: 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 task. As before, the --selectId argument selects warped HSC-R-band exposures while the --id argument specifies the patches that will make coadds for. Run: 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 again to make HSC-I-band coadds: 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 and 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.

Wrap up

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 command creates a sky map to encompass a given set of exposures.
  • The command warps exposures into the WCSs of the sky map.
  • The command coadds warped exposures into deep mosaics for a given patch and filter combination.
  • The --rerun rerunA:rerunB syntax lets you chain reruns together. Inputs are read from rerunA and outputs are written to rerunB.

Continue this tutorial in part 5, where you’ll measure sources in the coadds.