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. If splitSubfilters is set, then each subfilter will be further sub- divided during the forward modeling step (only). This approximates using a higher number of subfilters that may be necessary for high airmass observations, but does not increase the number of free parameters in the fit. This is needed when there are high airmass observations which would otherwise have significant DCR even within a subfilter. Because calculating the shifted images takes most of the time, splitting the subfilters is turned off by way of the splitThreshold option for low-airmass observations that do not suffer from DCR within a subfilter.

Attributes:

bufferSize : int

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

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.
applyModelWeights(modelImages, refImage, …)	Smoothly replace model pixel values with those from a reference at locations away from detected sources.
assembleMetadata(coaddExposure, …)	Set the metadata for the coadd.
assembleOnlineMeanCoadd(coaddExposure, …)	Assemble the coadd using the “online” method.
assembleSubregion(coaddExposure, bbox, …)	Assemble the coadd for a sub-region.
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, warpRefList, …)	Create a list of coadd exposures from a list of masked images.
filterArtifacts(spanSetList, …[, …])	Filter artifact candidates.
filterWarps(inputs, goodVisits)	Return list of only inputRefs with visitId in goodVisits ordered by goodVisit.
findArtifacts(templateCoadd, tempExpRefList, …)	Find artifacts.
getAllSchemaCatalogs()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
getBadPixelMask()	Convenience method to provide the bitmask from the mask plane names
getFullMetadata()	Get metadata for all tasks.
getFullName()	Get the task name as a hierarchical name including parent task names.
getName()	Get the name of the task.
getResourceConfig()	Return resource configuration for this task.
getSchemaCatalogs()	Get the schemas generated by this task.
getSkyInfo(patchRef)	Use getSkyinfo to return the skyMap, tract and patch information, wcs and the outer bbox of the patch.
getTaskDict()	Get a dictionary of all tasks as a shallow copy.
getTempExpDatasetName([warpType])	Return warp name for given warpType and task config
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.
makeSupplementaryDataGen3(butlerQC, …)	Deprecated since version v25.0.
measureCoaddPsf(coaddExposure)	Detect sources on the coadd exposure and measure the final PSF.
newModelFromResidual(dcrModels, …)	Calculate a new DcrModel from a set of image residuals.
prefilterArtifacts(spanSetList, exp)	Remove artifact candidates covered by bad mask plane.
prepareDcrInputs(templateCoadd, warpRefList, …)	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[, …])	Interpolate over missing data and mask bright stars.
removeMaskPlanes(maskedImage)	Unset the mask of an image for mask planes specified in the config.
run(skyInfo, warpRefList, imageScalerList, …)	Assemble the coadd.
runQuantum(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
selectCoaddPsf(templateCoadd, warpRefList)	Compute the PSF of the coadd from the exposures with the best seeing.
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.
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.

Attributes Documentation

canMultiprocess = True

Methods Documentation

applyAltEdgeMask(mask, altMaskList)

Propagate alt EDGE mask to SENSOR_EDGE AND INEXACT_PSF planes.

Parameters:

mask : lsst.afw.image.Mask

Original mask.

altMaskList : list of dict

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

applyAltMaskPlanes(mask, altMaskSpans)

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

Parameters:

mask : lsst.afw.image.Mask

Original mask.

altMaskSpans : dict

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

Returns:

mask : lsst.afw.image.Mask

Updated mask.

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.

assembleMetadata(coaddExposure, tempExpRefList, weightList)

Set the metadata for the coadd.

This basic implementation sets the filter from the first input.

Parameters:

coaddExposure : lsst.afw.image.Exposure

The target exposure for the coadd.

tempExpRefList : list

List of data references to tempExp.

weightList : list

List of weights.

Raises:

AssertionError

Raised if there is a length mismatch.

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

Assemble the coadd using the “online” method.

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

Parameters:

coaddExposure : lsst.afw.image.Exposure

The target exposure for the coadd.

tempExpRefList : list

List of data reference to tempExp.

imageScalerList : list

List of image scalers.

weightList : list

List of weights.

altMaskList : list

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

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd.

nImage : lsst.afw.image.ImageU, optional

Keeps track of exposure count for each pixel.

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

Assemble the coadd for a sub-region.

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

Parameters:

coaddExposure : lsst.afw.image.Exposure

The target exposure for the coadd.

bbox : lsst.geom.Box

Sub-region to coadd.

tempExpRefList : list

List of data reference to tempExp.

imageScalerList : list

List of image scalers.

weightList : list

List of weights.

altMaskList : list

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

statsFlags : lsst.afw.math.Property

Property object for statistic for coadd.

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd.

nImage : lsst.afw.image.ImageU, optional

Keeps track of exposure count for each pixel.

calculateConvergence(dcrModels, subExposures, bbox, warpRefList, 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.geom.box.Box2I

Sub-region to coadd.

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle

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.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, warpRefList, 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.geom.box.Box2I

Bounding box of the patch to coadd.

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle

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 1/(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, warpRefList, statsCtrl, convergenceMetric, 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.geom.box.Box2I

Bounding box of the subregion to coadd.

dcrBBox : lsst.geom.box.Box2I

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

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle

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.

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 1/(number of unmasked images contributing to each pixel).

dcrResiduals(residual, visitInfo, wcs, effectiveWavelength, bandwidth)

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.

Yields:

residualImage : numpy.ndarray

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

emptyMetadata() → None

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

fillCoadd(dcrModels, skyInfo, warpRefList, 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.

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle

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.PhotoCalib, optional

Scale factor to set the photometric calibration 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 of lsst.afw.geom.SpanSet

List of SpanSets representing artifact candidates.

epochCountImage : lsst.afw.image.Image

Image of accumulated number of warpDiff detections.

nImage : lsst.afw.image.ImageU

Image of the accumulated number of total epochs contributing.

Returns:

maskSpanSetList : list

List of SpanSets with artifacts.

filterWarps(inputs, goodVisits)

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

Parameters:

inputs : list of DeferredDatasetRef

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

goodVisit : dict

Dictionary with good visitIds as the keys. Value ignored.

Returns:

filteredInputs : list of DeferredDatasetRef

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

findArtifacts(templateCoadd, tempExpRefList, imageScalerList)

Find artifacts.

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

Parameters:

templateCoadd : lsst.afw.image.Exposure

Exposure to serve as model of static sky.

tempExpRefList : list

List of data references to warps.

imageScalerList : list

List of image scalers.

Returns:

altMasks : list of dict

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

getAllSchemaCatalogs() → Dict[str, Any]

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

Returns:

schemacatalogs : dict

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

Notes

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

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

getBadPixelMask()

Convenience method to provide the bitmask from the mask plane names

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

Get metadata for all tasks.

Returns:

metadata : TaskMetadata

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

Notes

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

topLevelTaskName:subtaskName:subsubtaskName.itemName

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

getFullName() → str

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

Returns:

fullName : str

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

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

getName() → str

Get the name of the task.

Returns:

taskName : str

Name of the task.

See also

getFullName

getResourceConfig() → Optional[ResourceConfig]

Return resource configuration for this task.

Returns:

Object of type ResourceConfig or None if resource

configuration is not defined for this task.

getSchemaCatalogs() → Dict[str, Any]

Get the schemas generated by this task.

Returns:

schemaCatalogs : dict

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

See also

Task.getAllSchemaCatalogs

Notes

Warning

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

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

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

getSkyInfo(patchRef)

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

Parameters:

patchRef : Unknown

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

Returns:

getSkyInfo : lsst.pipe.base.Struct

Sky Info as a struct with attributes:

skyMap

sky map (lsst.skyMap.SkyMap).

tractInfo

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

patchInfo

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

wcs

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

bbox

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

getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDict : dict

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

getTempExpDatasetName(warpType='direct')

Return warp name for given warpType and task config

Parameters:

warpType : str

Either ‘direct’ or ‘psfMatched’.

Returns:

WarpDatasetName : str

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

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

Parameters:

bbox : lsst.geom.box.Box2I

Sub-region to coadd.

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd.

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle

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: str) → lsst.pex.config.configurableField.ConfigurableField

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

Parameters:

doc : str

Help text for the field.

Returns:

configurableField : lsst.pex.config.ConfigurableField

A ConfigurableField for this task.

Examples

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

Here is an example of use:

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

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

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

Parameters:

name : str

Brief name of the subtask.

keyArgs

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

• “config”.
• “parentTask”.

Notes

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

makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)

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

measureCoaddPsf(coaddExposure)

Detect sources on the coadd exposure and measure the final PSF.

Parameters:

coaddExposure : lsst.afw.image.Exposure

The final coadded exposure.

newModelFromResidual(dcrModels, residualGeneratorList, dcrBBox, statsCtrl, 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.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.

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 1/(number of unmasked images contributing to each pixel).

Returns:

dcrModel : lsst.pipe.tasks.DcrModel

New model of the true sky after correcting chromatic effects.

prefilterArtifacts(spanSetList, exp)

Remove artifact candidates covered by bad mask plane.

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

Parameters:

spanSetList : list of lsst.afw.geom.SpanSet

List of SpanSets representing artifact candidates.

exp : lsst.afw.image.Exposure

Exposure containing mask planes used to prefilter.

Returns:

returnSpanSetList : list of lsst.afw.geom.SpanSet

List of SpanSets with artifacts.

prepareDcrInputs(templateCoadd, warpRefList, 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.

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle

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 : Struct

Results as a struct with attributes:

tempExprefList

list of data references to tempExp.

weightList

list of weightings.

imageScalerList

list of image scalers.

prepareStats(mask=None)

Prepare the statistics for coadding images.

Parameters:

mask : int, optional

Bit mask value to exclude from coaddition.

Returns:

stats : Struct

Statistics as a struct with attributes:

statsCtrl

Statistics control object for coadd (StatisticsControl).

statsFlags

Statistic for coadd (Property).

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

Interpolate over missing data and mask bright stars.

Parameters:

coaddExposure : lsst.afw.image.Exposure

The coadded exposure to process.

brightObjectMasks : lsst.afw.table or None, optional

Table of bright objects to mask.

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

Data identification.

removeMaskPlanes(maskedImage)

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

Parameters:

maskedImage : lsst.afw.image.MaskedImage

The masked image to be modified.

Raises:

InvalidParameterError

Raised if no mask plane with that name was found.

run(skyInfo, warpRefList, 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 coaddExposures.

Parameters:

skyInfo : lsst.pipe.base.Struct

Patch geometry information, from getSkyInfo

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle

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 attributes:

templateCoadd

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

Returns:

result : lsst.pipe.base.Struct

Results as a struct with attributes:

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(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.

selectCoaddPsf(templateCoadd, warpRefList)

Compute the PSF of the coadd from the exposures with the best seeing.

Parameters:

templateCoadd : lsst.afw.image.ExposureF

The initial coadd exposure before accounting for DCR.

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle

The data references to the input warped exposures.

Returns:

psf : lsst.meas.algorithms.CoaddPsf

The average PSF of the input exposures with the best seeing.

setBrightObjectMasks(exposure, brightObjectMasks, dataId=None)

Set the bright object masks.

Parameters:

exposure : lsst.afw.image.Exposure

Exposure under consideration.

brightObjectMasks : lsst.afw.table

Table of bright objects to mask.

dataId : lsst.daf.butler.DataId, optional

Data identifier dict for patch.

setInexactPsf(mask)

Set INEXACT_PSF mask plane.

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

Parameters:

mask : lsst.afw.image.Mask

Coadded exposure’s mask, modified in-place.

static setRejectedMaskMapping(statsCtrl)

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

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

Parameters:

statsCtrl : lsst.afw.math.StatisticsControl

Statistics control object for coadd.

Returns:

maskMap : list of tuple of int

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

shrinkValidPolygons(coaddInputs)

Shrink coaddInputs’ ccds’ ValidPolygons in place.

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

Parameters:

coaddInputs : lsst.afw.image.coaddInputs

Original mask.

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

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

Parameters:

name : str

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

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

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