CompareWarpAssembleCoaddTask

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

Bases: lsst.pipe.tasks.assembleCoadd.AssembleCoaddTask

Assemble a compareWarp coadded image from a set of warps by masking artifacts detected by comparing PSF-matched warps.

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’ which marks pixels in the individual warps suspected to contain an artifact. We populate this plane on the input warps by comparing PSF-matched warps with a PSF-matched median coadd which serves as a model of the static sky. Any group of pixels that deviates from the PSF-matched template coadd by more than config.detect.threshold sigma, is an artifact candidate. The candidates are then filtered to remove variable sources and sources that are difficult to subtract such as bright stars. This filter is configured using the config parameters temporalThreshold and spatialThreshold. The temporalThreshold is the maximum fraction of epochs that the deviation can appear in and still be considered an artifact. The spatialThreshold is the maximum fraction of pixels in the footprint of the deviation that appear in other epochs (where other epochs is defined by the temporalThreshold). If the deviant region meets this criteria of having a significant percentage of pixels that deviate in only a few epochs, these pixels have the ‘CLIPPED’ bit set in the mask. These 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.

CompareWarpAssembleCoaddTask sub-classes AssembleCoaddTask and instantiates AssembleCoaddTask as a subtask to generate the TemplateCoadd (the model of the static sky).

Notes

Debugging: This task supports the following debug variables: - saveCountIm

If True then save the Epoch Count Image as a fits file in the figPath

• figPath

  Path to save the debug fits images and figures

Attributes Summary

canMultiprocess

Methods Summary

applyAltEdgeMask(mask, altMaskList)	Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes.
applyAltMaskPlanes(mask, altMaskSpans)	Apply in place alt mask formatted as SpanSets to a mask.
assembleMetadata(coaddExposure, …)	Set the metadata for the coadd.
assembleOnlineMeanCoadd(coaddExposure, …)	Assemble the coadd using the “online” method.
assembleSubregion(coaddExposure, bbox, …)	Assemble the coadd for a sub-region.
emptyMetadata()	Empty (clear) the metadata for this Task and all sub-Tasks.
filterArtifacts(spanSetList, …[, …])	Filter artifact candidates.
filterWarps(inputs, goodVisits)	Return list of only inputRefs with visitId in goodVisits ordered by goodVisit.
findArtifacts(templateCoadd, tempExpRefList, …)	Find artifacts.
getAllSchemaCatalogs()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
getBadPixelMask()	Convenience method to provide the bitmask from the mask plane names
getFullMetadata()	Get metadata for all tasks.
getFullName()	Get the task name as a hierarchical name including parent task names.
getName()	Get the name of the task.
getResourceConfig()	Return resource configuration for this task.
getSchemaCatalogs()	Get the schemas generated by this task.
getSkyInfo(patchRef)	Use getSkyinfo to return the skyMap, tract and patch information, wcs and the outer bbox of the patch.
getTaskDict()	Get a dictionary of all tasks as a shallow copy.
getTempExpDatasetName([warpType])	Return warp name for given warpType and task config
makeField(doc)	Make a lsst.pex.config.ConfigurableField for this task.
makeSubtask(name, **keyArgs)	Create a subtask as a new instance as the name attribute of this task.
makeSupplementaryDataGen3(butlerQC, …)	Deprecated since version v25.0.
prefilterArtifacts(spanSetList, exp)	Remove artifact candidates covered by bad mask plane.
prepareInputs(refList)	Prepare the input warps for coaddition by measuring the weight for each warp and the scaling for the photometric zero point.
prepareStats([mask])	Prepare the statistics for coadding images.
processResults(coaddExposure[, …])	Interpolate over missing data and mask bright stars.
removeMaskPlanes(maskedImage)	Unset the mask of an image for mask planes specified in the config.
run(skyInfo, tempExpRefList, …)	Assemble a coadd from input warps.
runQuantum(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
setBrightObjectMasks(exposure, brightObjectMasks)	Set the bright object masks.
setInexactPsf(mask)	Set INEXACT_PSF mask plane.
setRejectedMaskMapping(statsCtrl)	Map certain mask planes of the warps to new planes for the coadd.
shrinkValidPolygons(coaddInputs)	Shrink coaddInputs’ ccds’ ValidPolygons in place.
timer(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess = True

Methods Documentation

applyAltEdgeMask(mask, altMaskList)

Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes.

Parameters:

mask : lsst.afw.image.Mask

Original mask.

altMaskList : list of dict

List of Dicts containing spanSet lists. Each element contains the new mask plane name (e.g. “CLIPPED and/or “NO_DATA”) as the key, and list of SpanSets to apply to the mask.

applyAltMaskPlanes(mask, altMaskSpans)

Apply in place alt mask formatted as SpanSets to a mask.

Parameters:

mask : lsst.afw.image.Mask

Original mask.

altMaskSpans : dict

SpanSet lists to apply. Each element contains the new mask plane name (e.g. “CLIPPED and/or “NO_DATA”) as the key, and list of SpanSets to apply to the mask.

Returns:

mask : lsst.afw.image.Mask

Updated mask.

assembleMetadata(coaddExposure, tempExpRefList, weightList)

Set the metadata for the coadd.

This basic implementation sets the filter from the first input.

Parameters:

coaddExposure : lsst.afw.image.Exposure

The target exposure for the coadd.

tempExpRefList : list

List of data references to tempExp.

weightList : list

List of weights.

Raises:

AssertionError

Raised if there is a length mismatch.

assembleOnlineMeanCoadd(coaddExposure, tempExpRefList, imageScalerList, weightList, altMaskList, statsCtrl, nImage=None)

Assemble the coadd using the “online” method.

This method takes a running sum of images and weights to save memory. It only works for MEAN statistics.

Parameters:

coaddExposure : lsst.afw.image.Exposure

The target exposure for the coadd.

tempExpRefList : list

List of data reference to tempExp.

imageScalerList : list

List of image scalers.

weightList : list

List of weights.

altMaskList : list

List of alternate masks to use rather than those stored with tempExp, or None. Each element is dict with keys = mask plane name to which to add the spans.

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd.

nImage : lsst.afw.image.ImageU, optional

Keeps track of exposure count for each pixel.

assembleSubregion(coaddExposure, bbox, tempExpRefList, imageScalerList, weightList, altMaskList, statsFlags, statsCtrl, nImage=None)

Assemble the coadd for a sub-region.

For each coaddTempExp, check for (and swap in) an alternative mask if one is passed. Remove mask planes listed in config.removeMaskPlanes. Finally, stack the actual exposures using lsst.afw.math.statisticsStack with the statistic specified by statsFlags. Typically, the statsFlag will be one of lsst.afw.math.MEAN for a mean-stack or lsst.afw.math.MEANCLIP for outlier rejection using an N-sigma clipped mean where N and iterations are specified by statsCtrl. Assign the stacked subregion back to the coadd.

Parameters:

coaddExposure : lsst.afw.image.Exposure

The target exposure for the coadd.

bbox : lsst.geom.Box

Sub-region to coadd.

tempExpRefList : list

List of data reference to tempExp.

imageScalerList : list

List of image scalers.

weightList : list

List of weights.

altMaskList : list

List of alternate masks to use rather than those stored with tempExp, or None. Each element is dict with keys = mask plane name to which to add the spans.

statsFlags : lsst.afw.math.Property

Property object for statistic for coadd.

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd.

nImage : lsst.afw.image.ImageU, optional

Keeps track of exposure count for each pixel.

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

filterArtifacts(spanSetList, epochCountImage, nImage, footprintsToExclude=None)

Filter artifact candidates.

Parameters:

spanSetList : list of lsst.afw.geom.SpanSet

List of SpanSets representing artifact candidates.

epochCountImage : lsst.afw.image.Image

Image of accumulated number of warpDiff detections.

nImage : lsst.afw.image.ImageU

Image of the accumulated number of total epochs contributing.

Returns:

maskSpanSetList : list

List of SpanSets with artifacts.

filterWarps(inputs, goodVisits)

Return list of only inputRefs with visitId in goodVisits ordered by goodVisit.

Parameters:

inputs : list of DeferredDatasetRef

List of lsst.pipe.base.DeferredDatasetRef with dataId containing visit.

goodVisit : dict

Dictionary with good visitIds as the keys. Value ignored.

Returns:

filteredInputs : list of DeferredDatasetRef

Filtered and sorted list of inputRefs with visitId in goodVisits ordered by goodVisit.

findArtifacts(templateCoadd, tempExpRefList, imageScalerList)

Find artifacts.

Loop through warps twice. The first loop builds a map with the count of how many epochs each pixel deviates from the templateCoadd by more than config.chiThreshold sigma. The second loop takes each difference image and filters the artifacts detected in each using count map to filter out variable sources and sources that are difficult to subtract cleanly.

Parameters:

templateCoadd : lsst.afw.image.Exposure

Exposure to serve as model of static sky.

tempExpRefList : list

List of data references to warps.

imageScalerList : list

List of image scalers.

Returns:

altMasks : list of dict

List of dicts containing information about CLIPPED (i.e., artifacts), NO_DATA, and EDGE pixels.

getAllSchemaCatalogs() → Dict[str, Any]

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:

schemacatalogs : dict

Keys are butler dataset type, values are a empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getBadPixelMask()

Convenience method to provide the bitmask from the mask plane names

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata

Get metadata for all tasks.

Returns:

metadata : TaskMetadata

The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str

Get the task name as a hierarchical name including parent task names.

Returns:

fullName : str

The full name consists of the name of the parent task and each subtask separated by periods. For example:

• The full name of top-level task “top” is simply “top”.
• The full name of subtask “sub” of top-level task “top” is “top.sub”.
• The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getName() → str

Get the name of the task.

Returns:

taskName : str

Name of the task.

See also

getFullName

getResourceConfig() → Optional[ResourceConfig]

Return resource configuration for this task.

Returns:

Object of type ResourceConfig or None if resource

configuration is not defined for this task.

getSchemaCatalogs() → Dict[str, Any]

Get the schemas generated by this task.

Returns:

schemaCatalogs : dict

Keys are butler dataset type, values are an empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for this task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getSkyInfo(patchRef)

Use getSkyinfo to return the skyMap, tract and patch information, wcs and the outer bbox of the patch.

Parameters:

patchRef : Unknown

Data reference for sky map. Must include keys “tract” and “patch”.

Returns:

getSkyInfo : lsst.pipe.base.Struct

Sky Info as a struct with attributes:

skyMap

sky map (lsst.skyMap.SkyMap).

tractInfo

Information for chosen tract of sky map (lsst.skymap.TractInfo).

patchInfo

Information about chosen patch of tract (lsst.skymap.PatchInfo).

wcs

WCS of tract (lsst.afw.image.SkyWcs).

bbox

Outer bbox of patch, as an geom Box2I (lsst.afw.geom.Box2I).

getTaskDict() → Dict[str, weakref]

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDict : dict

Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

getTempExpDatasetName(warpType='direct')

Return warp name for given warpType and task config

Parameters:

warpType : str

Either ‘direct’ or ‘psfMatched’.

Returns:

WarpDatasetName : str

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:

doc : str

Help text for the field.

Returns:

configurableField : lsst.pex.config.ConfigurableField

A ConfigurableField for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")

makeSubtask(name: str, **keyArgs) → None

Create a subtask as a new instance as the name attribute of this task.

Parameters:

name : str

Brief name of the subtask.

keyArgs

Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden:

• “config”.
• “parentTask”.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)

Deprecated since version v25.0: makeSupplementaryDataGen3 is deprecated in favor of _makeSupplementaryData

prefilterArtifacts(spanSetList, exp)

Remove artifact candidates covered by bad mask plane.

Any future editing of the candidate list that does not depend on temporal information should go in this method.

Parameters:

spanSetList : list of lsst.afw.geom.SpanSet

List of SpanSets representing artifact candidates.

exp : lsst.afw.image.Exposure

Exposure containing mask planes used to prefilter.

Returns:

returnSpanSetList : list of lsst.afw.geom.SpanSet

List of SpanSets with artifacts.

prepareInputs(refList)

Prepare the input warps for coaddition by measuring the weight for each warp and the scaling for the photometric zero point.

Each Warp has its own photometric zeropoint and background variance. Before coadding these Warps together, compute a scale factor to normalize the photometric zeropoint and compute the weight for each Warp.

Parameters:

refList : list

List of data references to tempExp.

Returns:

result : Struct

Results as a struct with attributes:

tempExprefList

list of data references to tempExp.

weightList

list of weightings.

imageScalerList

list of image scalers.

prepareStats(mask=None)

Prepare the statistics for coadding images.

Parameters:

mask : int, optional

Bit mask value to exclude from coaddition.

Returns:

stats : Struct

Statistics as a struct with attributes:

statsCtrl

Statistics control object for coadd (StatisticsControl).

statsFlags

Statistic for coadd (Property).

processResults(coaddExposure, brightObjectMasks=None, dataId=None)

Interpolate over missing data and mask bright stars.

Parameters:

coaddExposure : lsst.afw.image.Exposure

The coadded exposure to process.

brightObjectMasks : lsst.afw.table or None, optional

Table of bright objects to mask.

dataId : lsst.daf.butler.DataId or None, optional

Data identification.

removeMaskPlanes(maskedImage)

Unset the mask of an image for mask planes specified in the config.

Parameters:

maskedImage : lsst.afw.image.MaskedImage

The masked image to be modified.

Raises:

InvalidParameterError

Raised if no mask plane with that name was found.

run(skyInfo, tempExpRefList, imageScalerList, weightList, supplementaryData)

Assemble a coadd from input warps.

Assemble the coadd using the provided list of coaddTempExps. Since the full coadd covers a patch (a large area), the assembly is performed over small areas on the image at a time in order to conserve memory usage. Iterate over subregions within the outer bbox of the patch using assembleSubregion to stack the corresponding subregions from the coaddTempExps with the statistic specified. Set the edge bits the coadd mask based on the weight map.

Parameters:

skyInfo : Struct

Struct with geometric information about the patch.

tempExpRefList : list

List of data references to Warps (previously called CoaddTempExps).

imageScalerList : list

List of image scalers.

weightList : list

List of weights.

altMaskList : list, optional

List of alternate masks to use rather than those stored with tempExp.

mask : int, optional

Bit mask value to exclude from coaddition.

supplementaryData : Struct, optional

Struct with additional data products needed to assemble coadd. Only used by subclasses that implement _makeSupplementaryData and override run.

Returns:

result : Struct

Results as a struct with attributes:

coaddExposure

Coadded exposure (lsst.afw.image.Exposure).

nImage

Exposure count image (lsst.afw.image.Image), if requested.

inputMap

Bit-wise map of inputs, if requested.

warpRefList

Input list of refs to the warps (lsst.daf.butler.DeferredDatasetHandle) (unmodified).

imageScalerList

Input list of image scalers (list) (unmodified).

weightList

Input list of weights (list) (unmodified).

Assemble the coadd.

Find artifacts and apply them to the warps’ masks creating a list of

alternative masks with a new “CLIPPED” plane and updated “NO_DATA”

plane. Then pass these alternative masks to the base class’s run

method.

runQuantum(butlerQC, inputRefs, outputRefs)

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQC : ButlerQuantumContext

A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.

inputRefs : InputQuantizedConnection

Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.

outputRefs : OutputQuantizedConnection

Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

setBrightObjectMasks(exposure, brightObjectMasks, dataId=None)

Set the bright object masks.

Parameters:

exposure : lsst.afw.image.Exposure

Exposure under consideration.

brightObjectMasks : lsst.afw.table

Table of bright objects to mask.

dataId : lsst.daf.butler.DataId, optional

Data identifier dict for patch.

setInexactPsf(mask)

Set INEXACT_PSF mask plane.

If any of the input images isn’t represented in the coadd (due to clipped pixels or chip gaps), the CoaddPsf will be inexact. Flag these pixels.

Parameters:

mask : lsst.afw.image.Mask

Coadded exposure’s mask, modified in-place.

static setRejectedMaskMapping(statsCtrl)

Map certain mask planes of the warps to new planes for the coadd.

If a pixel is rejected due to a mask value other than EDGE, NO_DATA, or CLIPPED, set it to REJECTED on the coadd. If a pixel is rejected due to EDGE, set the coadd pixel to SENSOR_EDGE. If a pixel is rejected due to CLIPPED, set the coadd pixel to CLIPPED.

Parameters:

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd.

Returns:

maskMap : list of tuple of int

A list of mappings of mask planes of the warped exposures to mask planes of the coadd.

shrinkValidPolygons(coaddInputs)

Shrink coaddInputs’ ccds’ ValidPolygons in place.

Either modify each ccd’s validPolygon in place, or if CoaddInputs does not have a validPolygon, create one from its bbox.

Parameters:

coaddInputs : lsst.afw.image.coaddInputs

Original mask.

timer(name: str, logLevel: int = 10) → Iterator[None]

Context manager to log performance data for an arbitrary block of code.

Parameters:

name : str

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time