DcrAssembleCoaddTask

class lsst.pipe.tasks.dcrAssembleCoadd.DcrAssembleCoaddTask(*args, **kwargs)

Bases: lsst.pipe.tasks.assembleCoadd.CompareWarpAssembleCoaddTask

Assemble DCR coadded images from a set of warps.

Notes

As with AssembleCoaddTask, we want to assemble a coadded image from a set of Warps (also called coadded temporary exposures), including the effects of Differential Chromatic Refraction (DCR). For full details of the mathematics and algorithm, please see DMTN-037: DCR-matched template generation (https://dmtn-037.lsst.io).

This Task produces a DCR-corrected deepCoadd, as well as a dcrCoadd for each subfilter used in the iterative calculation. It begins by dividing the bandpass-defining filter into N equal bandwidth “subfilters”, and divides the flux in each pixel from an initial coadd equally into each as a “dcrModel”. Because the airmass and parallactic angle of each individual exposure is known, we can calculate the shift relative to the center of the band in each subfilter due to DCR. For each exposure we apply this shift as a linear transformation to the dcrModels and stack the results to produce a DCR-matched exposure. The matched exposures are subtracted from the input exposures to produce a set of residual images, and these residuals are reverse shifted for each exposures’ subfilters and stacked. The shifted and stacked residuals are added to the dcrModels to produce a new estimate of the flux in each pixel within each subfilter. The dcrModels are solved for iteratively, which continues until the solution from a new iteration improves by less than a set percentage, or a maximum number of iterations is reached. Two forms of regularization are employed to reduce unphysical results. First, the new solution is averaged with the solution from the previous iteration, which mitigates oscillating solutions where the model overshoots with alternating very high and low values. Second, a common degeneracy when the data have a limited range of airmass or parallactic angle values is for one subfilter to be fit with very low or negative values, while another subfilter is fit with very high values. This typically appears in the form of holes next to sources in one subfilter, and corresponding extended wings in another. Because each subfilter has a narrow bandwidth we assume that physical sources that are above the noise level will not vary in flux by more than a factor of frequencyClampFactor between subfilters, and pixels that have flux deviations larger than that factor will have the excess flux distributed evenly among all subfilters.

Attributes:

bufferSize : int

The number of pixels to grow each subregion by to allow for DCR.

Attributes Summary

canMultiprocess

Methods Summary

adaptArgsAndRun(inputData, inputDataIds, …)	Assemble a coadd from a set of Warps.
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.
applyModelWeights(modelImages, refImage, …)	Smoothly replace model pixel values with those from a reference at locations away from detected sources.
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.
calculateConvergence(dcrModels, …)	Calculate a quality of fit metric for the matched templates.
calculateGain(convergenceList, gainList)	Calculate the gain to use for the current iteration.
calculateModelWeights(dcrModels, dcrBBox)	Build an array that smoothly tapers to 0 away from detected sources.
calculateNImage(dcrModels, bbox, …)	Calculate the number of exposures contributing to each subfilter.
calculateSingleConvergence(dcrModels, …)	Calculate a quality of fit metric for a single matched template.
dcrAssembleSubregion(dcrModels, …)	Assemble the DCR coadd for a sub-region.
dcrResiduals(residual, visitInfo, wcs, …)	Prepare a residual image for stacking in each subfilter by applying the reverse DCR shifts.
emptyMetadata()	Empty (clear) the metadata for this Task and all sub-Tasks.
fillCoadd(dcrModels, skyInfo, …[, …])	Create a list of coadd exposures from a list of masked images.
filterArtifacts(spanSetList, …[, …])	Filter artifact candidates.
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
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
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.
loadSubExposures(bbox, statsCtrl, …)	Pre-load sub-regions of a list of exposures.
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 inputs specific to Subclass.
makeSupplementaryDataGen3(inputData, …)	Make inputs specific to Subclass with Gen 3 API
newModelFromResidual(dcrModels, …)	Calculate a new DcrModel from a set of image residuals.
parseAndRun([args, config, log, doReturnResults])	Parse an argument list and run the command.
prefilterArtifacts(spanSetList, exp)	Remove artifact candidates covered by bad mask plane.
prepareDcrInputs(templateCoadd, …)	Prepare the DCR coadd by iterating through the visitInfo of the input warps.
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.
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.
stackCoadd(dcrCoadds)	Add a list of sub-band coadds together.
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)

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.

applyModelWeights(modelImages, refImage, modelWeights)

Smoothly replace model pixel values with those from a reference at locations away from detected sources.

Parameters:

modelImages : list of lsst.afw.image.Image

The new DCR model images from the current iteration. The values will be modified in place.

refImage : lsst.afw.image.MaskedImage

A reference image used to supply the default pixel values.

modelWeights : numpy.ndarray or float

A 2D array of weight values that tapers smoothly to zero away from detected sources. Set to a placeholder value of 1.0 if self.config.useModelWeights is False.

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.afw.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.

calculateConvergence(dcrModels, subExposures, bbox, tempExpRefList, weightList, statsCtrl)

Calculate a quality of fit metric for the matched templates.

Parameters:

dcrModels : lsst.pipe.tasks.DcrModel

Best fit model of the true sky after correcting chromatic effects.

subExposures : dict of lsst.afw.image.ExposureF

The pre-loaded exposures for the current subregion.

bbox : lsst.afw.geom.box.Box2I

Sub-region to coadd

tempExpRefList : list of lsst.daf.persistence.ButlerDataRef

The data references to the input warped exposures.

weightList : list of float

The weight to give each input exposure in the coadd

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd

Returns:

convergenceMetric : float

Quality of fit metric for all input exposures, within the sub-region

calculateGain(convergenceList, gainList)

Calculate the gain to use for the current iteration.

After calculating a new DcrModel, each value is averaged with the value in the corresponding pixel from the previous iteration. This reduces oscillating solutions that iterative techniques are plagued by, and speeds convergence. By far the biggest changes to the model happen in the first couple iterations, so we can also use a more aggressive gain later when the model is changing slowly.

Parameters:

convergenceList : list of float

The quality of fit metric from each previous iteration.

gainList : list of float

The gains used in each previous iteration: appended with the new gain value. Gains are numbers between self.config.baseGain and 1.

Returns:

gain : float

Relative weight to give the new solution when updating the model. A value of 1.0 gives equal weight to both solutions.

Raises:

ValueError

If len(convergenceList) != len(gainList)+1.

calculateModelWeights(dcrModels, dcrBBox)

Build an array that smoothly tapers to 0 away from detected sources.

Parameters:

dcrModels : lsst.pipe.tasks.DcrModel

Best fit model of the true sky after correcting chromatic effects.

dcrBBox : lsst.afw.geom.box.Box2I

Sub-region of the coadd which includes a buffer to allow for DCR.

Returns:

weights : numpy.ndarray or float

A 2D array of weight values that tapers smoothly to zero away from detected sources. Set to a placeholder value of 1.0 if self.config.useModelWeights is False.

Raises:

ValueError

If useModelWeights is set and modelWeightsWidth is negative.

calculateNImage(dcrModels, bbox, tempExpRefList, spanSetMaskList, statsCtrl)

Calculate the number of exposures contributing to each subfilter.

Parameters:

dcrModels : lsst.pipe.tasks.DcrModel

Best fit model of the true sky after correcting chromatic effects.

bbox : lsst.afw.geom.box.Box2I

Bounding box of the patch to coadd.

tempExpRefList : list of lsst.daf.persistence.ButlerDataRef

The data references to the input warped exposures.

spanSetMaskList : list of dict containing spanSet lists, or None

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

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd

Returns:

dcrNImages : list of lsst.afw.image.ImageU

List of exposure count images for each subfilter

dcrWeights : list of lsst.afw.image.ImageF

Per-pixel weights for each subfilter. Equal to the number of unmasked images contributing to each pixel.

calculateSingleConvergence(dcrModels, exposure, significanceImage, statsCtrl)

Calculate a quality of fit metric for a single matched template.

Parameters:

dcrModels : lsst.pipe.tasks.DcrModel

Best fit model of the true sky after correcting chromatic effects.

exposure : lsst.afw.image.ExposureF

The input warped exposure to evaluate.

significanceImage : numpy.ndarray

Array of weights for each pixel corresponding to its significance for the convergence calculation.

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd

Returns:

convergenceMetric : float

Quality of fit metric for one exposure, within the sub-region.

dcrAssembleSubregion(dcrModels, subExposures, bbox, dcrBBox, tempExpRefList, statsCtrl, convergenceMetric, baseMask, gain, modelWeights, refImage, dcrWeights)

Assemble the DCR coadd for a sub-region.

Build a DCR-matched template for each input exposure, then shift the residuals according to the DCR in each subfilter. Stack the shifted residuals and apply them as a correction to the solution from the previous iteration. Restrict the new model solutions from varying by more than a factor of modelClampFactor from the last solution, and additionally restrict the individual subfilter models from varying by more than a factor of frequencyClampFactor from their average. Finally, mitigate potentially oscillating solutions by averaging the new solution with the solution from the previous iteration, weighted by their convergence metric.

Parameters:

dcrModels : lsst.pipe.tasks.DcrModel

Best fit model of the true sky after correcting chromatic effects.

subExposures : dict of lsst.afw.image.ExposureF

The pre-loaded exposures for the current subregion.

bbox : lsst.afw.geom.box.Box2I

Bounding box of the subregion to coadd.

dcrBBox : lsst.afw.geom.box.Box2I

Sub-region of the coadd which includes a buffer to allow for DCR.

tempExpRefList : list of lsst.daf.persistence.ButlerDataRef

The data references to the input warped exposures.

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd

convergenceMetric : float

Quality of fit metric for the matched templates of the input images.

baseMask : lsst.afw.image.Mask

Mask of the initial template coadd.

gain : float, optional

Relative weight to give the new solution when updating the model.

modelWeights : numpy.ndarray or float

A 2D array of weight values that tapers smoothly to zero away from detected sources. Set to a placeholder value of 1.0 if self.config.useModelWeights is False.

refImage : lsst.afw.image.Image

A reference image used to supply the default pixel values.

dcrWeights : list of lsst.afw.image.Image

Per-pixel weights for each subfilter. Equal to the number of unmasked images contributing to each pixel.

dcrResiduals(residual, visitInfo, wcs, filterInfo)

Prepare a residual image for stacking in each subfilter by applying the reverse DCR shifts.

Parameters:

residual : numpy.ndarray

The residual masked image for one exposure, after subtracting the matched template

visitInfo : lsst.afw.image.VisitInfo

Metadata for the exposure.

wcs : lsst.afw.geom.SkyWcs

Coordinate system definition (wcs) for the exposure.

filterInfo : lsst.afw.image.Filter

The filter definition, set in the current instruments’ obs package. Required for any calculation of DCR, including making matched templates.

Yields:

residualImage : numpy.ndarray

The residual image for the next subfilter, shifted for DCR.

emptyMetadata()

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

fillCoadd(dcrModels, skyInfo, tempExpRefList, weightList, calibration=None, coaddInputs=None, mask=None, variance=None)

Create a list of coadd exposures from a list of masked images.

Parameters:

dcrModels : lsst.pipe.tasks.DcrModel

Best fit model of the true sky after correcting chromatic effects.

skyInfo : lsst.pipe.base.Struct

Patch geometry information, from getSkyInfo

tempExpRefList : list of lsst.daf.persistence.ButlerDataRef

The data references to the input warped exposures.

weightList : list of float

The weight to give each input exposure in the coadd

calibration : lsst.afw.Image.Calib, optional

Scale factor to set the photometric zero point of an exposure.

coaddInputs : lsst.afw.Image.CoaddInputs, optional

A record of the observations that are included in the coadd.

mask : lsst.afw.image.Mask, optional

Optional mask to override the values in the final coadd.

variance : lsst.afw.image.Image, optional

Optional variance plane to override the values in the final coadd.

Returns:

dcrCoadds : list of lsst.afw.image.ExposureF

A list of coadd exposures, each exposure containing the model for one subfilter.

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.

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

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

getFullName

classmethod getOutputDatasetTypes(config)

Return output dataset type descriptors

Remove output dataset types not produced by the Task

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 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 afwGeom 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.

loadSubExposures(bbox, statsCtrl, tempExpRefList, imageScalerList, spanSetMaskList)

Pre-load sub-regions of a list of exposures.

Parameters:

bbox : lsst.afw.geom.box.Box2I

Sub-region to coadd

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd

tempExpRefList : list of lsst.daf.persistence.ButlerDataRef

The data references to the input warped exposures.

imageScalerList : list of lsst.pipe.task.ImageScaler

The image scalars correct for the zero point of the exposures.

spanSetMaskList : list of dict containing spanSet lists, or None

Each element is dict with keys = mask plane name to add the spans to

Returns:

subExposures : dict

The dict keys are the visit IDs, and the values are lsst.afw.image.ExposureF The pre-loaded exposures for the current subregion. The variance plane contains weights, and not the variance

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 inputs specific to Subclass.

Generate a templateCoadd to use as a native model of static sky to subtract from warps.

Parameters:

dataRef : lsst.daf.persistence.butlerSubset.ButlerDataRef

Butler dataRef for supplementary data.

selectDataList : list (optional)

Optional List of data references to Calexps.

warpRefList : list (optional)

Optional List of data references to 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(inputData, inputDataIds, outputDataIds, butler)

Make inputs specific to Subclass with Gen 3 API

Calls Gen3 adaptArgsAndRun instead of the Gen2 specific runDataRef

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

Result struct with components:

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

newModelFromResidual(dcrModels, residualGeneratorList, dcrBBox, statsCtrl, mask, gain, modelWeights, refImage, dcrWeights)

Calculate a new DcrModel from a set of image residuals.

Parameters:

dcrModels : lsst.pipe.tasks.DcrModel

Current model of the true sky after correcting chromatic effects.

residualGeneratorList : generator of numpy.ndarray

The residual image for the next subfilter, shifted for DCR.

dcrBBox : lsst.afw.geom.box.Box2I

Sub-region of the coadd which includes a buffer to allow for DCR.

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd

mask : lsst.afw.image.Mask

Mask to use for each new model image.

gain : float

Relative weight to give the new solution when updating the model.

modelWeights : numpy.ndarray or float

A 2D array of weight values that tapers smoothly to zero away from detected sources. Set to a placeholder value of 1.0 if self.config.useModelWeights is False.

refImage : lsst.afw.image.Image

A reference image used to supply the default pixel values.

dcrWeights : list of lsst.afw.image.Image

Per-pixel weights for each subfilter. Equal to the number of unmasked images contributing to each pixel.

Returns:

dcrModel : lsst.pipe.tasks.DcrModel

New model of the true sky after correcting chromatic effects.

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.
• parsedCmd: the parsed command returned by the argument parser’s lsst.pipe.base.ArgumentParser.parse_args method.
• 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.

  This will typically be a list of None unless doReturnResults is True; see Task.RunnerClass (TaskRunner by default) for more information.

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.

prepareDcrInputs(templateCoadd, tempExpRefList, weightList)

Prepare the DCR coadd by iterating through the visitInfo of the input warps.

Sets the property bufferSize.

Parameters:

templateCoadd : lsst.afw.image.ExposureF

The initial coadd exposure before accounting for DCR.

tempExpRefList : list of lsst.daf.persistence.ButlerDataRef

The data references to the input warped exposures.

weightList : list of float

The weight to give each input exposure in the coadd Will be modified in place if doAirmassWeight is set.

Returns:

dcrModels : lsst.pipe.tasks.DcrModel

Best fit model of the true sky after correcting chromatic effects.

Raises:

NotImplementedError

If lambdaMin is missing from the Mapper class of the obs package being used.

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, supplementaryData=None)

Assemble the coadd.

Requires additional inputs Struct supplementaryData to contain a templateCoadd that serves as the model of the static sky.

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 assemble method.

Divide the templateCoadd evenly between each subfilter of a DcrModel as the starting best estimate of the true wavelength- dependent sky. Forward model the DcrModel using the known chromatic effects in each subfilter and calculate a convergence metric based on how well the modeled template matches the input warps. If the convergence has not yet reached the desired threshold, then shift and stack the residual images to build a new DcrModel. Apply conditioning to prevent oscillating solutions between iterations or between subfilters.

Once the DcrModel reaches convergence or the maximum number of iterations has been reached, fill the metadata for each subfilter image and make them proper ``coaddExposure``s.

Parameters:

skyInfo : lsst.pipe.base.Struct

Patch geometry information, from getSkyInfo

tempExpRefList : list of lsst.daf.persistence.ButlerDataRef

The data references to the input warped exposures.

imageScalerList : list of lsst.pipe.task.ImageScaler

The image scalars correct for the zero point of the exposures.

weightList : list of float

The weight to give each input exposure in the coadd

supplementaryData : lsst.pipe.base.Struct

Result struct returned by makeSupplementaryData with components:

• templateCoadd: coadded exposure (lsst.afw.image.Exposure)

Returns:

result : lsst.pipe.base.Struct

Result struct with components:

• coaddExposure: coadded exposure (lsst.afw.image.Exposure)
• nImage: exposure count image (lsst.afw.image.ImageU)
• dcrCoadds: list of coadded exposures for each subfilter
• dcrNImages: list of exposure count images for each subfilter

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

Assemble a coadd from a set of warps.

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 method. Forward model chromatic effects across multiple subfilters, and subtract from the input Warps to build sets of residuals. Use the residuals to construct a new DcrModel for each subfilter, and iterate until the model converges. Interpolate over NaNs and optionally write the coadd to disk. Return the coadded exposure.

Parameters:

dataRef : lsst.daf.persistence.ButlerDataRef

Data reference defining the patch for coaddition and the reference Warp

selectDataList : list of lsst.daf.persistence.ButlerDataRef

List of data references to warps. Data to be coadded will be selected from this list based on overlap with the patch defined by the data reference.

Returns:

results : lsst.pipe.base.Struct

The Struct contains the following fields:

• coaddExposure: coadded exposure (lsst.afw.image.Exposure)
• nImage: exposure count image (lsst.afw.image.ImageU)
• dcrCoadds: list of coadded exposures for each subfilter
• dcrNImages: list of exposure count images for each subfilter

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 afwGeom.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.

stackCoadd(dcrCoadds)

Add a list of sub-band coadds together.

Parameters:

dcrCoadds : list of lsst.afw.image.ExposureF

A list of coadd exposures, each exposure containing the model for one subfilter.

Returns:

coaddExposure : lsst.afw.image.ExposureF

A single coadd exposure that is the sum of the sub-bands.

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.