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

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.

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

For example, put something like:

import lsstDebug
def DebugInfo(name):
    di = lsstDebug.getInfo(name)
    if name == "lsst.pipe.tasks.assembleCoadd":
        di.saveCountIm = True
        di.figPath = "/desired/path/to/debugging/output/images"
    return di
lsstDebug.Info = DebugInfo

into your debug.py file and run assemebleCoadd.py with the --debug flag. Some subtasks may have their own debug variables; see individual Task documentation.

Examples

CompareWarpAssembleCoaddTask assembles a set of warped images into a coadded image. The CompareWarpAssembleCoaddTask is invoked by running assembleCoadd.py with the flag --compareWarpCoadd. 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 warps 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 CompareWarpAssembleCoaddTask in the larger context of multi-band processing, we will generate the HSC-I & -R band oadds 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

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 --compareWarpCoadd $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.

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.
applyOverrides(config) A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.
assembleMetadata(coaddExposure, …) Set the metadata for the coadd.
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() !
getCoaddDatasetName([warpType]) Return coadd name for given warpType and task config
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) !
getTaskDict() Get a dictionary of all tasks as a shallow copy.
getTempExpDatasetName([warpType]) Return warp name for given warpType and task config
getTempExpRefList(patchRef, calExpRefList) Generate list data references corresponding to warped exposures that lie within the patch to be coadded.
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.
makeSupplementaryData(dataRef[, …]) Make additional inputs to run() specific to subclasses (Gen2)
makeSupplementaryDataGen3(butlerQC, …) Make additional inputs to run() specific to subclasses (Gen3)
parseAndRun([args, config, log, doReturnResults]) Parse an argument list and run the command.
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.
readBrightObjectMasks(dataRef) Retrieve the bright object masks.
removeMaskPlanes(maskedImage) Unset the mask of an image for mask planes specified in the config.
run(skyInfo, tempExpRefList, …) Assemble a coadd from input warps
runDataRef(dataRef[, selectDataList, …]) Assemble a coadd from a set of Warps.
runQuantum(butlerQC, inputRefs, outputRefs) Method to do butler IO and or transforms to provide in memory objects for tasks run method
selectExposures(patchRef[, skyInfo, …]) !
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.
writeConfig(butler[, clobber, doBackup]) Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.
writeMetadata(dataRef) Write the metadata produced from processing the data.
writePackageVersions(butler[, clobber, …]) Compare and write package versions.
writeSchemas(butler[, clobber, doBackup]) Write the schemas returned by lsst.pipe.base.Task.getAllSchemaCatalogs.

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

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.

classmethod applyOverrides(config)

A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.

Parameters:
config : instance of task’s ConfigClass

Task configuration.

Notes

This is necessary in some cases because the camera-specific overrides may retarget subtasks, wiping out changes made in ConfigClass.setDefaults. See LSST Trac ticket #2282 for more discussion.

Warning

This is called by CmdLineTask.parseAndRun; other ways of constructing a config will not apply these overrides.

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.

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()

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

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

Filter artifact candidates.

Parameters:
spanSetList : list

List of SpanSets representing artifact candidates.

epochCountImage : lsst.afw.image.Image

Image of accumulated number of warpDiff detections.

nImage : lsst.afw.image.Image

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

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

goodVisit : dict

Dictionary with good visitIds as the keys. Value ignored.

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

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

getAllSchemaCatalogs()

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()

! @brief Convenience method to provide the bitmask from the mask plane names

getCoaddDatasetName(warpType='direct')

Return coadd name for given warpType and task config

Parameters:
warpType : string

Either ‘direct’ or ‘psfMatched’

Returns:
CoaddDatasetName : string
getFullMetadata()

Get metadata for all tasks.

Returns:
metadata : lsst.daf.base.PropertySet

The PropertySet 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()

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()

Get the name of the task.

Returns:
taskName : str

Name of the task.

See also

getFullName

getResourceConfig()

Return resource configuration for this task.

Returns:
Object of type `~config.ResourceConfig` or ``None`` if resource
configuration is not defined for this task.
getSchemaCatalogs()

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)

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

@param[in] patchRef data reference for sky map. Must include keys “tract” and “patch”

@return pipe_base Struct containing: - skyMap: sky map - tractInfo: information for chosen tract of sky map - patchInfo: information about chosen patch of tract - wcs: WCS of tract - bbox: outer bbox of patch, as an geom Box2I

getTaskDict()

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 : string

Either ‘direct’ or ‘psfMatched’

Returns:
WarpDatasetName : string
getTempExpRefList(patchRef, calExpRefList)

Generate list data references corresponding to warped exposures that lie within the patch to be coadded.

Parameters:
patchRef : dataRef

Data reference for patch.

calExpRefList : list

List of data references for input calexps.

Returns:
tempExpRefList : list

List of Warp/CoaddTempExp data references.

classmethod makeField(doc)

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, **keyArgs)

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.

makeSupplementaryData(dataRef, selectDataList=None, warpRefList=None)

Make additional inputs to run() specific to subclasses (Gen2)

Duplicates interface of runDataRef method Available to be implemented by subclasses only if they need the coadd dataRef for performing preliminary processing before assembling the coadd.

Parameters:
dataRef : lsst.daf.persistence.ButlerDataRef

Butler data reference for supplementary data.

selectDataList : list (optional)

Optional List of data references to Calexps.

warpRefList : list (optional)

Optional List of data references to Warps.

Generate a templateCoadd to use as a naive model of static sky to
subtract from PSF-Matched warps.
Returns:
result : lsst.pipe.base.Struct

Result struct with components:

  • templateCoadd: coadded exposure (lsst.afw.image.Exposure)
  • nImage: N Image (lsst.afw.image.Image)
makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)

Make additional inputs to run() specific to subclasses (Gen3)

Duplicates interface of runQuantum method. Available to be implemented by subclasses only if they need the coadd dataRef for performing preliminary processing before assembling the coadd.

Parameters:
butlerQC : lsst.pipe.base.ButlerQuantumContext

Gen3 Butler object for fetching additional data products before running the Task specialized for quantum being processed

inputRefs : lsst.pipe.base.InputQuantizedConnection

Attributes are the names of the connections describing input dataset types. Values are DatasetRefs that task consumes for corresponding dataset type. DataIds are guaranteed to match data objects in inputData.

outputRefs : lsst.pipe.base.OutputQuantizedConnection

Attributes are the names of the connections describing output dataset types. Values are DatasetRefs that task is to produce for corresponding dataset type.

Generate a templateCoadd to use as a naive model of static sky to
subtract from PSF-Matched warps.
Returns:
result : lsst.pipe.base.Struct

Result struct with components:

  • templateCoadd : coadded exposure (lsst.afw.image.Exposure)
  • nImage : N Image (lsst.afw.image.Image)
classmethod parseAndRun(args=None, config=None, log=None, doReturnResults=False)

Parse an argument list and run the command.

Parameters:
args : list, optional

List of command-line arguments; if None use sys.argv.

config : lsst.pex.config.Config-type, optional

Config for task. If None use Task.ConfigClass.

log : lsst.log.Log-type, optional

Log. If None use the default log.

doReturnResults : bool, optional

If True, return the results of this task. Default is False. This is only intended for unit tests and similar use. It can easily exhaust memory (if the task returns enough data and you call it enough times) and it will fail when using multiprocessing if the returned data cannot be pickled.

Returns:
struct : lsst.pipe.base.Struct

Fields are:

argumentParser

the argument parser (lsst.pipe.base.ArgumentParser).

parsedCmd

the parsed command returned by the argument parser’s parse_args method (argparse.Namespace).

taskRunner

the task runner used to run the task (an instance of Task.RunnerClass).

resultList

results returned by the task runner’s run method, one entry per invocation (list). This will typically be a list of Struct, each containing at least an exitStatus integer (0 or 1); see Task.RunnerClass (TaskRunner by default) for more details.

Notes

Calling this method with no arguments specified is the standard way to run a command-line task from the command-line. For an example see pipe_tasks bin/makeSkyMap.py or almost any other file in that directory.

If one or more of the dataIds fails then this routine will exit (with a status giving the number of failed dataIds) rather than returning this struct; this behaviour can be overridden by specifying the --noExit command-line option.

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

List of SpanSets representing artifact candidates.

exp : lsst.afw.image.Exposure

Exposure containing mask planes used to prefilter.

Returns:
returnSpanSetList : list

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 : lsst.pipe.base.Struct

Result struct with components:

  • 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 : lsst.pipe.base.Struct

Statistics structure with the following fields:

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.

dataRef : lsst.daf.persistence.ButlerDataRef

Butler data reference for supplementary data.

readBrightObjectMasks(dataRef)

Retrieve the bright object masks.

Returns None on failure.

Parameters:
dataRef : lsst.daf.persistence.butlerSubset.ButlerDataRef

A Butler dataRef.

Returns:
result : lsst.daf.persistence.butlerSubset.ButlerDataRef

Bright object mask from the Butler object, or None if it cannot be retrieved.

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.

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

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 : lsst.pipe.base.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 : lsst.pipe.base.Struct, optional

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

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), if requested.
  • inputMap: bit-wise map of inputs, if requested.
  • warpRefList: input list of refs to the warps (
    lsst.daf.butler.DeferredDatasetHandle or lsst.daf.persistence.ButlerDataRef) (unmodified)
  • imageScalerList: input list of image scalers (unmodified)
  • weightList: input list of weights (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.
The input parameters ``supplementaryData`` is a `lsst.pipe.base.Struct`
that must contain a ``templateCoadd`` that serves as the
model of the static sky.
runDataRef(dataRef, selectDataList=None, warpRefList=None)

Assemble a coadd from a set of Warps.

Pipebase.CmdlineTask entry point to Coadd a set of Warps. Compute weights to be applied to each Warp and find scalings to match the photometric zeropoint to a reference Warp. Assemble the Warps using run. Interpolate over NaNs and optionally write the coadd to disk. Return the coadded exposure.

Parameters:
dataRef : lsst.daf.persistence.butlerSubset.ButlerDataRef

Data reference defining the patch for coaddition and the reference Warp (if config.autoReference=False). Used to access the following data products: - self.config.coaddName + "Coadd_skyMap" - self.config.coaddName + "Coadd_ + <warpType> + "Warp" (optionally) - self.config.coaddName + "Coadd"

selectDataList : list

List of data references to Calexps. Data to be coadded will be selected from this list based on overlap with the patch defined by dataRef, grouped by visit, and converted to a list of data references to warps.

warpRefList : list

List of data references to Warps to be coadded. Note: warpRefList is just the new name for tempExpRefList.

Returns:
retStruct : lsst.pipe.base.Struct

Result struct with components:

  • coaddExposure: coadded exposure (Exposure).
  • nImage: exposure count image (Image).
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.

Notes

Assemble a coadd from a set of Warps.

PipelineTask (Gen3) entry point to Coadd a set of Warps. Analogous to runDataRef, it prepares all the data products to be passed to run, and processes the results before returning a struct of results to be written out. AssembleCoadd cannot fit all Warps in memory. Therefore, its inputs are accessed subregion by subregion by the Gen3 DeferredDatasetHandle that is analagous to the Gen2 lsst.daf.persistence.ButlerDataRef. Any updates to this method should correspond to an update in runDataRef while both entry points are used.

selectExposures(patchRef, skyInfo=None, selectDataList=[])

! @brief Select exposures to coadd

Get the corners of the bbox supplied in skyInfo using @ref geom.Box2D and convert the pixel positions of the bbox corners to sky coordinates using @ref skyInfo.wcs.pixelToSky. Use the @ref WcsSelectImagesTask_ “WcsSelectImagesTask” to select exposures that lie inside the patch indicated by the dataRef.

@param[in] patchRef data reference for sky map patch. Must include keys “tract”, “patch”,
plus the camera-specific filter key (e.g. “filter” or “band”)

@param[in] skyInfo geometry for the patch; output from getSkyInfo @return a list of science exposures to coadd, as butler data references

setBrightObjectMasks(exposure, brightObjectMasks, dataId=None)

Set the bright object masks.

Parameters:
exposure : lsst.afw.image.Exposure

Exposure under consideration.

dataId : lsst.daf.persistence.dataId

Data identifier dict for patch.

brightObjectMasks : lsst.afw.table

Table of bright objects to mask.

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, logLevel=10000)

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 lsst.log level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time
writeConfig(butler, clobber=False, doBackup=True)

Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to write the config. The config is written to dataset type CmdLineTask._getConfigName.

clobber : bool, optional

A boolean flag that controls what happens if a config already has been saved:

  • True: overwrite or rename the existing config, depending on doBackup.
  • False: raise TaskError if this config does not match the existing config.
doBackup : bool, optional

Set to True to backup the config files if clobbering.

writeMetadata(dataRef)

Write the metadata produced from processing the data.

Parameters:
dataRef

Butler data reference used to write the metadata. The metadata is written to dataset type CmdLineTask._getMetadataName.

writePackageVersions(butler, clobber=False, doBackup=True, dataset='packages')

Compare and write package versions.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to read/write the package versions.

clobber : bool, optional

A boolean flag that controls what happens if versions already have been saved:

  • True: overwrite or rename the existing version info, depending on doBackup.
  • False: raise TaskError if this version info does not match the existing.
doBackup : bool, optional

If True and clobbering, old package version files are backed up.

dataset : str, optional

Name of dataset to read/write.

Raises:
TaskError

Raised if there is a version mismatch with current and persisted lists of package versions.

Notes

Note that this operation is subject to a race condition.

writeSchemas(butler, clobber=False, doBackup=True)

Write the schemas returned by lsst.pipe.base.Task.getAllSchemaCatalogs.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to write the schema. Each schema is written to the dataset type specified as the key in the dict returned by getAllSchemaCatalogs.

clobber : bool, optional

A boolean flag that controls what happens if a schema already has been saved:

  • True: overwrite or rename the existing schema, depending on doBackup.
  • False: raise TaskError if this schema does not match the existing schema.
doBackup : bool, optional

Set to True to backup the schema files if clobbering.

Notes

If clobber is False and an existing schema does not match a current schema, then some schemas may have been saved successfully and others may not, and there is no easy way to tell which is which.