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.
`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.
`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`()	!
`getCoaddDatasetName`([warpType])	Return coadd name for given warpType and task config
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getName`()	Get the name of the task.
`getResourceConfig`()	Return resource configuration for this task.
`getSchemaCatalogs`()	Get the schemas generated by this task.
`getSkyInfo`(patchRef)	!
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`getTempExpDatasetName`([warpType])	Return warp name for given warpType and task config
`getTempExpRefList`(patchRef, calExpRefList)	Generate list data references corresponding to warped exposures that lie within the patch to be coadded.
`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 additional inputs to run() specific to subclasses (Gen2)
`makeSupplementaryDataGen3`(butlerQC, …)	Make additional inputs to run() specific to subclasses (Gen3)
`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.
`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, 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.
`readBrightObjectMasks`(dataRef)	Retrieve the bright object masks.
`removeMaskPlanes`(maskedImage)	Unset the mask of an image for mask planes specified in the config.
`run`(skyInfo, warpRefList, imageScalerList, …)	Assemble the coadd.
`runDataRef`(dataRef[, selectDataList, …])	Assemble a coadd from a set of warps.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
`selectCoaddPsf`(templateCoadd, warpRefList)	Compute the PSF of the coadd from the exposures with the best seeing.
`selectExposures`(patchRef[, skyInfo, …])	!
`setBrightObjectMasks`(exposure, brightObjectMasks)	Set the bright object masks.
`setInexactPsf`(mask)	Set INEXACT_PSF mask plane.
`setRejectedMaskMapping`(statsCtrl)	Map certain mask planes of the warps to new planes for the coadd.
`shrinkValidPolygons`(coaddInputs)	Shrink coaddInputs’ ccds’ ValidPolygons in place.
`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

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.

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 or: 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.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 or: 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 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 or: 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.
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 or: 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.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` List of SpanSets representing artifact candidates. epochCountImage : `lsst.afw.image.Image` Image of accumulated number of warpDiff detections. nImage : `lsst.afw.image.Image` Image of the accumulated number of total epochs contributing.
Returns:	maskSpanSetList : `list` List of SpanSets with artifacts.

filterWarps(inputs, goodVisits)¶

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

Parameters:	inputs : list List of `lsst.pipe.base.DeferredDatasetRef` with dataId containing visit goodVisit : `dict` Dictionary with good visitIds as the keys. Value ignored.

findArtifacts(templateCoadd, tempExpRefList, imageScalerList)¶

Find artifacts.

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

Parameters:	templateCoadd : `lsst.afw.image.Exposure` Exposure to serve as model of static sky. tempExpRefList : `list` List of data references to warps. imageScalerList : `list` List of image scalers.
Returns:	altMasks : `list` List of dicts containing information about CLIPPED (i.e., artifacts), NO_DATA, and EDGE pixels.

getAllSchemaCatalogs() → 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()¶: ! @brief Convenience method to provide the bitmask from the mask plane names

getCoaddDatasetName(warpType='direct')¶

Return coadd name for given warpType and task config

Parameters:	warpType : string Either ‘direct’ or ‘psfMatched’
Returns:	CoaddDatasetName : `string`

getFullMetadata() → 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

Task.getAllSchemaCatalogs

Notes

Warning

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

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

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

getSkyInfo(patchRef)¶

! @brief Use @ref coaddBase::getSkyInfo “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() → Dict[str, weakref]¶

Get a dictionary of all tasks as a shallow copy.

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

getTempExpDatasetName(warpType='direct')¶

Return warp name for given warpType and task config

Parameters:	warpType : 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, 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 or: 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: 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.

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

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

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

Parameters:	dataRef : `lsst.daf.persistence.ButlerDataRef` Butler data reference for supplementary data. selectDataList : `list` (optional) Optional List of data references to Calexps. warpRefList : `list` (optional) Optional List of data references to Warps. Generate a templateCoadd to use as a naive model of static sky to subtract from PSF-Matched warps.
Returns:	result : `lsst.pipe.base.Struct` Result struct with components: `templateCoadd`: coadded exposure (`lsst.afw.image.Exposure`) `nImage`: N Image (`lsst.afw.image.Image`)

makeSupplementaryDataGen3(butlerQC, inputRefs, outputRefs)¶

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

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

Parameters:

butlerQC : lsst.pipe.base.ButlerQuantumContext: Gen3 Butler object for fetching additional data products before running the Task specialized for quantum being processed
inputRefs : lsst.pipe.base.InputQuantizedConnection: Attributes are the names of the connections describing input dataset types. Values are DatasetRefs that task consumes for corresponding dataset type. DataIds are guaranteed to match data objects in inputData.
outputRefs : lsst.pipe.base.OutputQuantizedConnection: Attributes are the names of the connections describing output dataset types. Values are DatasetRefs that task is to produce for corresponding dataset type.
Load the previously-generated template coadd.
This can be removed entirely once we no longer support the Gen 2 butler.

Returns:

templateCoadd : lsst.pipe.base.Struct

Result struct with components:

templateCoadd: coadded exposure (lsst.afw.image.ExposureF)

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.

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 : logging.Logger-type, optional: Log. If None use the default log.
doReturnResults : bool, optional: If True, return the results of this task. Default is False. This is only intended for unit tests and similar use. It can easily exhaust memory (if the task returns enough data and you call it enough times) and it will fail when using multiprocessing if the returned data cannot be pickled.

Returns:

struct : lsst.pipe.base.Struct

Fields are:

argumentParser: the argument parser (lsst.pipe.base.ArgumentParser).
parsedCmd: the parsed command returned by the argument parser’s parse_args method (argparse.Namespace).
taskRunner: the task runner used to run the task (an instance of Task.RunnerClass).
resultList: results returned by the task runner’s run method, one entry per invocation (list). This will typically be a list of Struct, each containing at least an exitStatus integer (0 or 1); see Task.RunnerClass (TaskRunner by default) for more details.

Notes

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

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

prefilterArtifacts(spanSetList, exp)¶

Remove artifact candidates covered by bad mask plane.

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

Parameters:	spanSetList : `list` List of SpanSets representing artifact candidates. exp : `lsst.afw.image.Exposure` Exposure containing mask planes used to prefilter.
Returns:	returnSpanSetList : `list` List of SpanSets with artifacts.

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` or `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, brightObjectMasks=None, dataId=None)¶

Interpolate over missing data and mask bright stars.

Parameters:	coaddExposure : `lsst.afw.image.Exposure` The coadded exposure to process. dataRef : `lsst.daf.persistence.ButlerDataRef` Butler data reference for supplementary data.

readBrightObjectMasks(dataRef)¶

Retrieve the bright object masks.

Returns None on failure.

Parameters:	dataRef : `lsst.daf.persistence.butlerSubset.ButlerDataRef` A Butler dataRef.
Returns:	result : `lsst.daf.persistence.butlerSubset.ButlerDataRef` Bright object mask from the Butler object, or None if it cannot be retrieved.

removeMaskPlanes(maskedImage)¶

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

Parameters:	maskedImage : `lsst.afw.image.MaskedImage` The masked image to be modified.

run(skyInfo, 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 ``coaddExposure``s.

Parameters:

skyInfo : lsst.pipe.base.Struct

Patch geometry information, from getSkyInfo

warpRefList : list of lsst.daf.butler.DeferredDatasetHandle or

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(butlerQC, inputRefs, outputRefs)¶

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

Parameters:

butlerQC : ButlerQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefs : InputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.
outputRefs : OutputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

Notes

Assemble a coadd from a set of Warps.

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

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` or `lsst.daf.persistence.ButlerDataRef` 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.

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 afw::geom::SkyWcs::pixelToSky “skyInfo.wcs.pixelToSky”. Use the @ref selectImages::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 @param[in] selectDataList list of @ref selectImages::SelectStruct “SelectStruct”

to consider for selection

@return a list of science exposures to coadd, as butler data references

setBrightObjectMasks(exposure, brightObjectMasks, dataId=None)¶

Set the bright object masks.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure under consideration. dataId : `lsst.daf.persistence.dataId` Data identifier dict for patch. brightObjectMasks : `lsst.afw.table` Table of bright objects to mask.

setInexactPsf(mask)¶

Set INEXACT_PSF mask plane.

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

Parameters:	mask : `lsst.afw.image.Mask` Coadded exposure’s mask, modified in-place.

static setRejectedMaskMapping(statsCtrl)¶

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

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

Parameters:	statsCtrl : `lsst.afw.math.StatisticsControl` Statistics control object for coadd
Returns:	maskMap : `list` of `tuple` of `int` A list of mappings of mask planes of the warped exposures to mask planes of the coadd.

shrinkValidPolygons(coaddInputs)¶

Shrink coaddInputs’ ccds’ ValidPolygons in place.

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

Parameters:	coaddInputs : `lsst.afw.image.coaddInputs` Original mask.

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.

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DcrAssembleCoaddTask¶