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.

Attributes Summary

canMultiprocess

Methods Summary

`adaptArgsAndRun`(inputData, inputDataIds, …)	Assemble a coadd from a set of Warps.
`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.
`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.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`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
`getDatasetTypes`(config, configClass)	Return dataset type descriptors defined in task configuration.
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getInitInputDatasetTypes`(config)	Return dataset type descriptors that can be used to retrieve the `initInputs` constructor argument.
`getInitOutputDatasetTypes`(config)	Return dataset type descriptors that can be used to write the objects returned by `getOutputDatasets`.
`getInitOutputDatasets`()	Return persistable outputs that are available immediately after the task has been constructed.
`getInputDatasetTypes`(config)	Return input dataset type descriptors
`getName`()	Get the name of the task.
`getOutputDatasetTypes`(config)	Return output dataset type descriptors
`getPerDatasetTypeDimensions`(config)	Return any Dimensions that are permitted to have different values for different DatasetTypes within the same quantum.
`getPrerequisiteDatasetTypes`(config)	Return the local names of input dataset types that should be assumed to exist instead of constraining what data to process with this 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`(inputData, …)	Make additional inputs to run() specific to subclasses (Gen3)
`parseAndRun`([args, config, log, doReturnResults])	Parse an argument list and run the command.
`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, dataRef)	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 the coadd for a region.
`runDataRef`(dataRef[, selectDataList, …])	Assemble a coadd from a set of Warps.
`runQuantum`(quantum, butler)	Execute PipelineTask algorithm on single quantum of data.
`saveStruct`(struct, outputDataRefs, butler)	Save data in butler.
`selectExposures`(patchRef[, skyInfo, …])	!
`setBrightObjectMasks`(exposure, dataId, …)	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

adaptArgsAndRun(inputData, inputDataIds, outputDataIds, butler)¶

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 to struct of results to be written out. AssembleCoadd cannot fit all Warps in memory. Therefore, its inputs are accessed subregion by subregion by the lsst.daf.butler.ShimButler that quacks like a Gen2 lsst.daf.persistence.Butler. Updates to this method should correspond to an update in runDataRef while both entry points are used.

Parameters:

inputData : dict: Keys are the names of the configs describing input dataset types. Values are input Python-domain data objects (or lists of objects) retrieved from data butler.
inputDataIds : dict: Keys are the names of the configs describing input dataset types. Values are DataIds (or lists of DataIds) that task consumes for corresponding dataset type.
outputDataIds : dict: Keys are the names of the configs describing input dataset types. Values are DataIds (or lists of DataIds) that task is to produce for corresponding dataset type.
butler : lsst.daf.butler.Butler: Gen3 Butler object for fetching additional data products before running the Task

Returns:

result : lsst.pipe.base.Struct

Result struct with components:

coaddExposure : coadded exposure (lsst.afw.image.Exposure)
nImage: N Image (lsst.afw.image.Image)

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.

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.geom.Box: BBox of the coadd and warps.

Returns:

bigFootprintsCoadd : list: List of big footprints

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

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`

classmethod getDatasetTypes(config, configClass)¶

Return dataset type descriptors defined in task configuration.

This method can be used by other methods that need to extract dataset types from task configuration (e.g. getInputDatasetTypes or sub-class methods).

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration. configClass : `type` Class of the configuration object which defines dataset type.
Returns:	Dictionary where key is the name (arbitrary) of the output dataset and value is the `DatasetTypeDescriptor` instance. Default implementation uses configuration field name as dictionary key. Returns empty dict if configuration has no fields with the specified ``configClass``.

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”.

classmethod getInitInputDatasetTypes(config)¶

Return dataset type descriptors that can be used to retrieve the initInputs constructor argument.

Datasets used in initialization may not be associated with any Dimension (i.e. their data IDs must be empty dictionaries).

Default implementation finds all fields of type InitInputInputDatasetConfig in configuration (non-recursively) and uses them for constructing DatasetTypeDescriptor instances. The names of these fields are used as keys in returned dictionary. Subclasses can override this behavior.

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration.
Returns:	Dictionary where key is the name (arbitrary) of the input dataset and value is the `DatasetTypeDescriptor` instance. Default implementation uses configuration field name as dictionary key. When the task requires no initialization inputs, should return an empty dict.

classmethod getInitOutputDatasetTypes(config)¶

Return dataset type descriptors that can be used to write the objects returned by getOutputDatasets.

Datasets used in initialization may not be associated with any Dimension (i.e. their data IDs must be empty dictionaries).

Default implementation finds all fields of type InitOutputDatasetConfig in configuration (non-recursively) and uses them for constructing DatasetTypeDescriptor instances. The names of these fields are used as keys in returned dictionary. Subclasses can override this behavior.

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration.
Returns:	Dictionary where key is the name (arbitrary) of the output dataset and value is the `DatasetTypeDescriptor` instance. Default implementation uses configuration field name as dictionary key. When the task produces no initialization outputs, should return an empty dict.

getInitOutputDatasets()¶

Return persistable outputs that are available immediately after the task has been constructed.

Subclasses that operate on catalogs should override this method to return the schema(s) of the catalog(s) they produce.

It is not necessary to return the PipelineTask’s configuration or other provenance information in order for it to be persisted; that is the responsibility of the execution system.

Returns:	datasets : `dict` Dictionary with keys that match those of the dict returned by `getInitOutputDatasetTypes` values that can be written by calling `Butler.put` with those DatasetTypes and no data IDs. An empty `dict` should be returned by tasks that produce no initialization outputs.

classmethod getInputDatasetTypes(config)¶

Return input dataset type descriptors

Remove input dataset types not used by the Task

getName()¶

Get the name of the task.

Returns:	taskName : `str` Name of the task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implemenation 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("a brief description of what this task does")

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 pex_config 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` List of data references to Warps.

makeSupplementaryDataGen3(inputData, inputDataIds, outputDataIds, butler)¶

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

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

Parameters:

inputData : dict: Keys are the names of the configs describing input dataset types. Values are input Python-domain data objects (or lists of objects) retrieved from data butler.
inputDataIds : dict: Keys are the names of the configs describing input dataset types. Values are DataIds (or lists of DataIds) that task consumes for corresponding dataset type. DataIds are guaranteed to match data objects in inputData.
outputDataIds : dict: Keys are the names of the configs describing input dataset types. Values are DataIds (or lists of DataIds) that task is to produce for corresponding dataset type.
butler : lsst.daf.butler.Butler: Gen3 Butler object for fetching additional data products before running the Task

Returns:

result : lsst.pipe.base.Struct: Contains whatever additional data the subclass’s run method needs

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.

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: `statsCtrl`: Statistics control object for coadd (`lsst.afw.math.StatisticsControl`) `statsFlags`: Statistic for coadd (`lsst.afw.math.Property`)

processResults(coaddExposure, dataRef)¶

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, *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.

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(quantum, butler)¶

Execute PipelineTask algorithm on single quantum of data.

Typical implementation of this method will use inputs from quantum to retrieve Python-domain objects from data butler and call adaptArgsAndRun method on that data. On return from adaptArgsAndRun this method will extract data from returned Struct instance and save that data to butler.

The Struct returned from adaptArgsAndRun is expected to contain data attributes with the names equal to the names of the configuration fields defining output dataset types. The values of the data attributes must be data objects corresponding to the DataIds of output dataset types. All data objects will be saved in butler using DataRefs from Quantum’s output dictionary.

This method does not return anything to the caller, on errors corresponding exception is raised.

Parameters:	quantum : `Quantum` Object describing input and output corresponding to this invocation of PipelineTask instance. butler : object Data butler instance.
Raises:	`ScalarError` if a dataset type is configured as scalar but receives multiple DataIds in `quantum`. Any exceptions that happen in data butler or in `adaptArgsAndRun` method.

saveStruct(struct, outputDataRefs, butler)¶

Save data in butler.

Convention is that struct returned from run() method has data field(s) with the same names as the config fields defining output DatasetTypes. Subclasses may override this method to implement different convention for Struct content or in case any post-processing of data may be needed.

Parameters:	struct : `Struct` Data produced by the task packed into `Struct` instance outputDataRefs : `dict` Dictionary whose keys are the names of the configuration fields describing output dataset types and values are lists of DataRefs. DataRefs must match corresponding data objects in `struct` in number and order. butler : object Data butler instance.

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, dataId, brightObjectMasks)¶

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.

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SafeClipAssembleCoaddTask¶