SafeClipAssembleCoaddTask¶

class lsst.pipe.tasks.assembleCoadd.SafeClipAssembleCoaddTask(*args, **kwargs)¶

Bases: lsst.pipe.tasks.assembleCoadd.AssembleCoaddTask

Assemble a coadded image from a set of coadded temporary exposures, being careful to clip & flag areas with potential artifacts.

In AssembleCoaddTask, we compute the coadd as an clipped mean (i.e., we clip outliers). The problem with doing this is that when computing the coadd PSF at a given location, individual visit PSFs from visits with outlier pixels contribute to the coadd PSF and cannot be treated correctly. In this task, we correct for this behavior by creating a new badMaskPlane ‘CLIPPED’. We populate this plane on the input coaddTempExps and the final coadd where

difference imaging suggests that there is an outlier and

this outlier appears on only one or two images.

Such regions will not contribute to the final coadd. Furthermore, any routine to determine the coadd PSF can now be cognizant of clipped regions. Note that the algorithm implemented by this task is preliminary and works correctly for HSC data. Parameter modifications and or considerable redesigning of the algorithm is likley required for other surveys.

SafeClipAssembleCoaddTask uses a SourceDetectionTask “clipDetection” subtask and also sub-classes AssembleCoaddTask. You can retarget the SourceDetectionTask “clipDetection” subtask if you wish.

Notes

The lsst.pipe.base.CmdLineTask interface supports a flag -d to import debug.py from your PYTHONPATH; see baseDebug for more about debug.py files. SafeClipAssembleCoaddTask has no debug variables of its own. The SourceDetectionTask “clipDetection” subtasks may support debug variables. See the documetation for SourceDetectionTask “clipDetection” for further information.

Examples

SafeClipAssembleCoaddTask assembles a set of warped coaddTempExp images into a coadded image. The SafeClipAssembleCoaddTask is invoked by running assembleCoadd.py without the flag ‘–legacyCoadd’.

Usage of assembleCoadd.py expects a data reference to the tract patch and filter to be coadded (specified using ‘–id = [KEY=VALUE1[^VALUE2[^VALUE3…] [KEY=VALUE1[^VALUE2[^VALUE3…] …]]’) along with a list of coaddTempExps to attempt to coadd (specified using ‘–selectId [KEY=VALUE1[^VALUE2[^VALUE3…] [KEY=VALUE1[^VALUE2[^VALUE3…] …]]’). Only the coaddTempExps that cover the specified tract and patch will be coadded. A list of the available optional arguments can be obtained by calling assembleCoadd.py with the –help command line argument:

assembleCoadd.py --help

To demonstrate usage of the SafeClipAssembleCoaddTask in the larger context of multi-band processing, we will generate the HSC-I & -R band coadds from HSC engineering test data provided in the ci_hsc package. To begin, assuming that the lsst stack has been already set up, we must set up the obs_subaru and ci_hsc packages. This defines the environment variable $CI_HSC_DIR and points at the location of the package. The raw HSC data live in the $CI_HSC_DIR/raw directory. To begin assembling the coadds, we must first

processCcd

process the individual ccds in $CI_HSC_RAW to produce calibrated exposures
makeSkyMap

create a skymap that covers the area of the sky present in the raw exposures
makeCoaddTempExp

warp the individual calibrated exposures to the tangent plane of the coadd</DD>

We can perform all of these steps by running

$CI_HSC_DIR scons warp-903986 warp-904014 warp-903990 warp-904010 warp-903988

This will produce warped coaddTempExps for each visit. To coadd the warped data, we call assembleCoadd.py as follows:

assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-I        --selectId visit=903986 ccd=16 --selectId visit=903986 ccd=22 --selectId visit=903986 ccd=23        --selectId visit=903986 ccd=100--selectId visit=904014 ccd=1 --selectId visit=904014 ccd=6        --selectId visit=904014 ccd=12 --selectId visit=903990 ccd=18 --selectId visit=903990 ccd=25        --selectId visit=904010 ccd=4 --selectId visit=904010 ccd=10 --selectId visit=904010 ccd=100        --selectId visit=903988 ccd=16 --selectId visit=903988 ccd=17 --selectId visit=903988 ccd=23        --selectId visit=903988 ccd=24

This will process the HSC-I band data. The results are written in $CI_HSC_DIR/DATA/deepCoadd-results/HSC-I.

You may also choose to run:

scons warp-903334 warp-903336 warp-903338 warp-903342 warp-903344 warp-903346 nnn
assembleCoadd.py $CI_HSC_DIR/DATA --id patch=5,4 tract=0 filter=HSC-R --selectId visit=903334 ccd=16        --selectId visit=903334 ccd=22 --selectId visit=903334 ccd=23 --selectId visit=903334 ccd=100        --selectId visit=903336 ccd=17 --selectId visit=903336 ccd=24 --selectId visit=903338 ccd=18        --selectId visit=903338 ccd=25 --selectId visit=903342 ccd=4 --selectId visit=903342 ccd=10        --selectId visit=903342 ccd=100 --selectId visit=903344 ccd=0 --selectId visit=903344 ccd=5        --selectId visit=903344 ccd=11 --selectId visit=903346 ccd=1 --selectId visit=903346 ccd=6        --selectId visit=903346 ccd=12

to generate the coadd for the HSC-R band if you are interested in following multiBand Coadd processing as discussed in pipeTasks_multiBand.

Methods Summary

`buildDifferenceImage`(skyInfo, …)	Return an exposure that contains the difference between unclipped and clipped coadds.
`detectClip`(exp, tempExpRefList)	Detect clipped regions on an exposure and set the mask on the individual tempExp masks.
`detectClipBig`(clipList, clipFootprints, …)	Return individual warp footprints for large artifacts and append them to `clipList` in place.
`run`(skyInfo, tempExpRefList, …)	Assemble the coadd for a region.

Methods Documentation

buildDifferenceImage(skyInfo, tempExpRefList, imageScalerList, weightList)¶

Return an exposure that contains the difference between unclipped and clipped coadds.

Generate a difference image between clipped and unclipped coadds. Compute the difference image by subtracting an outlier-clipped coadd from an outlier-unclipped coadd. Return the difference image.

Parameters:	skyInfo : `lsst.pipe.base.Struct` Patch geometry information, from getSkyInfo tempExpRefList : `list` List of data reference to tempExp imageScalerList : `list` List of image scalers weightList : `list` List of weights
Returns:	exp : `lsst.afw.image.Exposure` Difference image of unclipped and clipped coadd wrapped in an Exposure

detectClip(exp, tempExpRefList)¶

Detect clipped regions on an exposure and set the mask on the individual tempExp masks.

Detect footprints in the difference image after smoothing the difference image with a Gaussian kernal. Identify footprints that overlap with one or two input coaddTempExps by comparing the computed overlap fraction to thresholds set in the config. A different threshold is applied depending on the number of overlapping visits (restricted to one or two). If the overlap exceeds the thresholds, the footprint is considered “CLIPPED” and is marked as such on the coaddTempExp. Return a struct with the clipped footprints, the indices of the coaddTempExps that end up overlapping with the clipped footprints, and a list of new masks for the coaddTempExps.

Parameters:

exp : lsst.afw.image.Exposure: Exposure to run detection on.
tempExpRefList : list: List of data reference to tempExp.

Returns:

result : lsst.pipe.base.Struct

Result struct with components:

clipFootprints: list of clipped footprints.
clipIndices: indices for each clippedFootprint in

tempExpRefList.
clipSpans: List of dictionaries containing spanSet lists

to clip. Each element contains the new maskplane name (“CLIPPED”) as the key and list of SpanSets as the value.
detectionFootprints: List of DETECTED/DETECTED_NEGATIVE plane

compressed into footprints.

detectClipBig(clipList, clipFootprints, clipIndices, detectionFootprints, maskClipValue, maskDetValue, coaddBBox)¶

Return individual warp footprints for large artifacts and append them to clipList in place.

Identify big footprints composed of many sources in the coadd difference that may have originated in a large diffuse source in the coadd. We do this by indentifying all clipped footprints that overlap significantly with each source in all the coaddTempExps.

Parameters:

clipList : list: List of alt mask SpanSets with clipping information. Modified.
clipFootprints : list: List of clipped footprints.
clipIndices : list: List of which entries in tempExpClipList each footprint belongs to.
maskClipValue: Mask value of clipped pixels.
maskDetValue: Mask value of detected pixels.
coaddBBox : lsst.afw.geom.Box: BBox of the coadd and warps.

Returns:

bigFootprintsCoadd : list: List of big footprints

run(skyInfo, tempExpRefList, imageScalerList, weightList, *args, **kwargs)¶

Assemble the coadd for a region.

Compute the difference of coadds created with and without outlier rejection to identify coadd pixels that have outlier values in some individual visits. Detect clipped regions on the difference image and mark these regions on the one or two individual coaddTempExps where they occur if there is significant overlap between the clipped region and a source. This leaves us with a set of footprints from the difference image that have been identified as having occured on just one or two individual visits. However, these footprints were generated from a difference image. It is conceivable for a large diffuse source to have become broken up into multiple footprints acrosss the coadd difference in this process. Determine the clipped region from all overlapping footprints from the detected sources in each visit - these are big footprints. Combine the small and big clipped footprints and mark them on a new bad mask plane. Generate the coadd using AssembleCoaddTask.run without outlier removal. Clipped footprints will no longer make it into the coadd because they are marked in the new bad mask plane.

Parameters:	skyInfo : `lsst.pipe.base.Struct` Patch geometry information, from getSkyInfo tempExpRefList : `list` List of data reference to tempExp imageScalerList : `list` List of image scalers weightList : `list` List of weights
Returns:	result : `lsst.pipe.base.Struct` Result struct with components: `coaddExposure`: coadded exposure (`lsst.afw.image.Exposure`). `nImage`: exposure count image (`lsst.afw.image.Image`).

Notes

args and kwargs are passed but ignored in order to match the call signature expected by the parent task.

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SafeClipAssembleCoaddTask¶