DcrAssembleCoaddTask¶
- class lsst.pipe.tasks.dcrAssembleCoadd.DcrAssembleCoaddTask(*args, **kwargs)¶
Bases:
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. IfsplitSubfilters
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 thesplitThreshold
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.
- bufferSize
Attributes Summary
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.
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.
Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
!
getCoaddDatasetName
([warpType])Return coadd name for given warpType and task config
Get metadata for all tasks.
Get the task name as a hierarchical name including parent task names.
getName
()Get the name of the task.
Return resource configuration for this task.
Get the schemas generated by this task.
getSkyInfo
(patchRef)!
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
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 ofSpanSets
to apply to the mask.
- 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.
- mask
- Returns:
- mask
lsst.afw.image.Mask
Updated mask.
- mask
- applyModelWeights(modelImages, refImage, modelWeights)¶
Smoothly replace model pixel values with those from a reference at locations away from detected sources.
- Parameters:
- modelImages
list
oflsst.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
orfloat
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.
- modelImages
- 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:
- configinstance of task’s
ConfigClass
Task configuration.
- configinstance of task’s
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.
- 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.
- coaddExposure
- 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 usinglsst.afw.math.statisticsStack
with the statistic specified by statsFlags. Typically, the statsFlag will be one of lsst.afw.math.MEAN for a mean-stack orlsst.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.
- coaddExposure
- 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
oflsst.afw.image.ExposureF
The pre-loaded exposures for the current subregion.
- bbox
lsst.geom.box.Box2I
Sub-region to coadd
- warpRefList
list
oflsst.daf.butler.DeferredDatasetHandle
or lsst.daf.persistence.ButlerDataRef
The data references to the input warped exposures.- weightList
list
offloat
The weight to give each input exposure in the coadd
- statsCtrl
lsst.afw.math.StatisticsControl
Statistics control object for coadd
- dcrModels
- Returns:
- convergenceMetric
float
Quality of fit metric for all input exposures, within the sub-region
- convergenceMetric
- 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:
- 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.
- gain
- 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.
- dcrModels
- Returns:
- weights
numpy.ndarray
orfloat
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.
- weights
- Raises:
- ValueError
If
useModelWeights
is set andmodelWeightsWidth
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
oflsst.daf.butler.DeferredDatasetHandle
or lsst.daf.persistence.ButlerDataRef
The data references to the input warped exposures.- spanSetMaskList
list
ofdict
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
- dcrModels
- Returns:
- 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
- dcrModels
- Returns:
- convergenceMetric
float
Quality of fit metric for one exposure, within the sub-region.
- convergenceMetric
- 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 offrequencyClampFactor
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
oflsst.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
oflsst.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
orfloat
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
oflsst.afw.image.Image
Per-pixel weights for each subfilter. Equal to 1/(number of unmasked images contributing to each pixel).
- dcrModels
- 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.
- residual
- Yields:
- residualImage
numpy.ndarray
The residual image for the next subfilter, shifted for DCR.
- residualImage
- 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
oflsst.daf.butler.DeferredDatasetHandle
or lsst.daf.persistence.ButlerDataRef
The data references to the input warped exposures.- weightList
list
offloat
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.
- dcrModels
- Returns:
- dcrCoadds
list
oflsst.afw.image.ExposureF
A list of coadd exposures, each exposure containing the model for one subfilter.
- dcrCoadds
- 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.
- spanSetList
- Returns:
- maskSpanSetList
list
List of SpanSets with artifacts.
- maskSpanSetList
- filterWarps(inputs, goodVisits)¶
Return list of only inputRefs with visitId in goodVisits ordered by goodVisit
- Parameters:
- inputslist
List of
lsst.pipe.base.connections.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:
- Returns:
- altMasks
list
List of dicts containing information about CLIPPED (i.e., artifacts), NO_DATA, and EDGE pixels.
- altMasks
- 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.
- schemacatalogs
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:
- warpTypestring
Either ‘direct’ or ‘psfMatched’
- Returns:
- CoaddDatasetName
string
- CoaddDatasetName
- getFullMetadata() 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.
- metadata
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”.
- fullName
- getResourceConfig() ResourceConfig | None ¶
Return resource configuration for this task.
- Returns:
- Object of type
ResourceConfig
orNone
if resource - configuration is not defined for this task.
- Object of type
- 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.
- schemaCatalogs
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, ReferenceType[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.
- taskDict
- getTempExpDatasetName(warpType='direct')¶
Return warp name for given warpType and task config
- Parameters:
- warpTypestring
Either ‘direct’ or ‘psfMatched’
- Returns:
- WarpDatasetName
string
- WarpDatasetName
- getTempExpRefList(patchRef, calExpRefList)¶
Generate list data references corresponding to warped exposures that lie within the patch to be coadded.
- 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
oflsst.daf.butler.DeferredDatasetHandle
or lsst.daf.persistence.ButlerDataRef
The data references to the input warped exposures.- imageScalerList
list
oflsst.pipe.task.ImageScaler
The image scalars correct for the zero point of the exposures.
- spanSetMaskList
list
ofdict
containing spanSet lists, or None Each element is dict with keys = mask plane name to add the spans to
- bbox
- Returns:
- classmethod makeField(doc: str) ConfigurableField ¶
Make a
lsst.pex.config.ConfigurableField
for this task.- Parameters:
- doc
str
Help text for the field.
- doc
- Returns:
- configurableField
lsst.pex.config.ConfigurableField
A
ConfigurableField
for this task.
- configurableField
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: Any) 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”.
- name
Notes
The subtask must be defined by
Task.config.name
, an instance ofConfigurableField
orRegistryField
.
- 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.
- dataRef
- Returns:
- result
lsst.pipe.base.Struct
Result struct with components:
templateCoadd
: coadded exposure (lsst.afw.image.Exposure
)nImage
: N Image (lsst.afw.image.Image
)
- result
- 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.
- butlerQC
- Returns:
- templateCoadd
lsst.pipe.base.Struct
Result struct with components:
templateCoadd
: coadded exposure (lsst.afw.image.ExposureF
)
- templateCoadd
- measureCoaddPsf(coaddExposure)¶
Detect sources on the coadd exposure and measure the final PSF.
- Parameters:
- coaddExposure
lsst.afw.image.Exposure
The final coadded exposure.
- coaddExposure
- 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
ofnumpy.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
orfloat
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
oflsst.afw.image.Image
Per-pixel weights for each subfilter. Equal to 1/(number of unmasked images contributing to each pixel).
- dcrModels
- Returns:
- dcrModel
lsst.pipe.tasks.DcrModel
New model of the true sky after correcting chromatic effects.
- dcrModel
- classmethod parseAndRun(args=None, config=None, log=None, doReturnResults=False)¶
Parse an argument list and run the command.
- Parameters:
- args
list
, optional - config
lsst.pex.config.Config
-type, optional Config for task. If
None
useTask.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 isFalse
. 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.
- args
- 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 ofStruct
, each containing at least anexitStatus
integer (0 or 1); seeTask.RunnerClass
(TaskRunner
by default) for more details.
- struct
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.
- 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
oflsst.daf.butler.DeferredDatasetHandle
or lsst.daf.persistence.ButlerDataRef
The data references to the input warped exposures.- weightList
list
offloat
The weight to give each input exposure in the coadd Will be modified in place if
doAirmassWeight
is set.
- templateCoadd
- Returns:
- dcrModels
lsst.pipe.tasks.DcrModel
Best fit model of the true sky after correcting chromatic effects.
- dcrModels
- 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
- refList
- Returns:
- result
lsst.pipe.base.Struct
Result struct with components:
- result
- prepareStats(mask=None)¶
Prepare the statistics for coadding images.
- Parameters:
- mask
int
, optional Bit mask value to exclude from coaddition.
- mask
- Returns:
- stats
lsst.pipe.base.Struct
Statistics structure with the following fields:
statsCtrl
: Statistics control object for coadd
statsFlags
: Statistic for coadd (lsst.afw.math.Property
)
- stats
- 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.
- coaddExposure
- readBrightObjectMasks(dataRef)¶
Retrieve the bright object masks.
Returns None on failure.
- Parameters:
- dataRef
lsst.daf.persistence.butlerSubset.ButlerDataRef
A Butler dataRef.
- dataRef
- Returns:
- result
lsst.daf.persistence.butlerSubset.ButlerDataRef
Bright object mask from the Butler object, or None if it cannot be retrieved.
- result
- 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.
- maskedImage
- run(skyInfo, warpRefList, imageScalerList, weightList, supplementaryData=None)¶
Assemble the coadd.
Requires additional inputs Struct
supplementaryData
to contain atemplateCoadd
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 aDcrModel
as the starting best estimate of the true wavelength- dependent sky. Forward model theDcrModel
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 newDcrModel
. 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
oflsst.daf.butler.DeferredDatasetHandle
or lsst.daf.persistence.ButlerDataRef
The data references to the input warped exposures.- imageScalerList
list
oflsst.pipe.task.ImageScaler
The image scalars correct for the zero point of the exposures.
- weightList
list
offloat
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
)
- skyInfo
- Returns:
- result
lsst.pipe.base.Struct
Result struct with components:
- result
- 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
oflsst.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.
- dataRef
- Returns:
- results
lsst.pipe.base.Struct
The Struct contains the following fields:
- results
- 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 thelsst.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 thelsst.daf.butler.DatasetRef
objects associated with the defined output connections.
- butlerQC
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 torun
, 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 Gen3DeferredDatasetHandle
that is analagous to the Gen2lsst.daf.persistence.ButlerDataRef
. Any updates to this method should correspond to an update inrunDataRef
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
oflsst.daf.butler.DeferredDatasetHandle
or lsst.daf.persistence.ButlerDataRef
The data references to the input warped exposures.
- templateCoadd
- Returns:
- psf
lsst.meas.algorithms.CoaddPsf
The average PSF of the input exposures with the best seeing.
- psf
- 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.
- exposure
- 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.
- mask
- 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
- statsCtrl
- Returns:
- 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.
- coaddInputs
- stackCoadd(dcrCoadds)¶
Add a list of sub-band coadds together.
- Parameters:
- dcrCoadds
list
oflsst.afw.image.ExposureF
A list of coadd exposures, each exposure containing the model for one subfilter.
- dcrCoadds
- Returns:
- coaddExposure
lsst.afw.image.ExposureF
A single coadd exposure that is the sum of the sub-bands.
- coaddExposure
- timer(name: str, logLevel: int = 10) Iterator[None] ¶
Context manager to log performance data for an arbitrary block of code.
- Parameters:
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:
- doBackup
bool
, optional Set to
True
to backup the config files if clobbering.
- butler
- 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:
- doBackup
bool
, optional If
True
and clobbering, old package version files are backed up.- dataset
str
, optional Name of dataset to read/write.
- butler
- 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:
- doBackup
bool
, optional Set to
True
to backup the schema files if clobbering.
- butler
Notes
If
clobber
isFalse
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.