ForcedPhotCoaddTask¶

class lsst.meas.base.ForcedPhotCoaddTask(butler=None, refSchema=None, initInputs=None, **kwds)¶

Bases: PipelineTask, CmdLineTask

A command-line driver for performing forced measurement on coadd images.

Parameters:

butlerlsst.daf.persistence.butler.Butler, optional: A Butler which will be passed to the references subtask to allow it to load its schema from disk. Optional, but must be specified if refSchema is not; if both are specified, refSchema takes precedence.
refSchemalsst.afw.table.Schema, optional: The schema of the reference catalog, passed to the constructor of the references subtask. Optional, but must be specified if butler is not; if both are specified, refSchema takes precedence.
**kwds: Keyword arguments are passed to the supertask constructor.

Attributes Summary

`canMultiprocess`
`dataPrefix`

Methods Summary

`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.
`attachFootprints`(sources, refCat, exposure, ...)	Attach Footprints to source records.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`fetchReferences`(dataRef, exposure)	Return an iterable of reference sources which overlap the exposure.
`generateMeasCat`(exposureDataId, exposure, ...)	Generate a measurement catalog for Gen3.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`getExposure`(dataRef)	Read input exposure on which measurement will be performed.
`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`()	The schema catalogs that will be used by this task.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`makeField`(doc)	Make a `lsst.pex.config.ConfigurableField` for this task.
`makeIdFactory`(dataRef, exposureId)	Create an object that generates globally unique source IDs.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`parseAndRun`([args, config, log, doReturnResults])	Parse an argument list and run the command.
`run`(measCat, exposure, refCat, refWcs[, ...])	Perform forced measurement on a single exposure.
`runDataRef`(dataRef[, psfCache])	Perform forced measurement on a single exposure.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
`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.
`writeOutput`(dataRef, sources)	Write forced source table
`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: ClassVar[bool] = True¶

dataPrefix = 'deepCoadd_'¶

Methods Documentation

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.

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.

attachFootprints(sources, refCat, exposure, refWcs, dataRef)¶

Attach Footprints to source records.

For coadd forced photometry, we use the deblended “heavy” Footprints from the single-band measurements of the same band - because we’ve guaranteed that the peaks (and hence child sources) will be consistent across all bands before we get to measurement, this should yield reasonable deblending for most sources. It’s most likely limitation is that it will not provide good flux upper limits for sources that were not detected in this band but were blended with sources that were.

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

fetchReferences(dataRef, exposure)¶

Return an iterable of reference sources which overlap the exposure.

Parameters:

dataReflsst.daf.persistence.ButlerDataRef: Butler data reference corresponding to the image to be measured; should have tract, patch, and filter keys.
exposurelsst.afw.image.Exposure: Unused.

Notes

All work is delegated to the references subtask; see CoaddSrcReferencesTask for information about the default behavior.

generateMeasCat(exposureDataId, exposure, refCat, refCatInBand, refWcs, idPackerName, footprintData)¶

Generate a measurement catalog for Gen3.

Parameters:

exposureDataIdDataId: Butler dataId for this exposure.
exposurelsst.afw.image.exposure.Exposure: Exposure to generate the catalog for.
refCatlsst.afw.table.SourceCatalog: Catalog of shapes and positions at which to force photometry.
refCatInBandlsst.afw.table.SourceCatalog: Catalog of shapes and position in the band forced photometry is currently being performed
refWcslsst.afw.image.SkyWcs: Reference world coordinate system.
idPackerNamestr: Type of ID packer to construct from the registry.
footprintDataScarletDataModel or lsst.afw.table.SourceCatalog: Either the scarlet data models or the deblended catalog containing footprints. If footprintData is None then the footprints contained in refCatInBand are used.

Returns:

measCatlsst.afw.table.SourceCatalog: Catalog of forced sources to measure.
expIdint: Unique binary id associated with the input exposure

Raises:

LookupError: Raised if a footprint with a given source id was in the reference catalog but not in the reference catalog in band (meaning there was some sort of mismatch in the two input catalogs)

getAllSchemaCatalogs() → Dict[str, Any]¶

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

Returns:

schemacatalogsdict: 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.

getExposure(dataRef)¶

Read input exposure on which measurement will be performed.

Parameters:

dataReflsst.daf.persistence.ButlerDataRef: Butler data reference.

getFullMetadata() → TaskMetadata¶

Get metadata for all tasks.

Returns:

metadataTaskMetadata: 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:

fullNamestr

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:

taskNamestr: 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()¶

The schema catalogs that will be used by this task.

Returns:

schemaCatalogsdict: Dictionary mapping dataset type to schema catalog.

Notes

There is only one schema for each type of forced measurement. The dataset type for this measurement is defined in the mapper.

getTaskDict() → Dict[str, ReferenceType[Task]]¶

Get a dictionary of all tasks as a shallow copy.

Returns:

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

classmethod makeField(doc: str) → ConfigurableField¶

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

Parameters:

docstr: Help text for the field.

Returns:

configurableFieldlsst.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")

makeIdFactory(dataRef, exposureId)¶

Create an object that generates globally unique source IDs.

Source IDs are created based on a per-CCD ID and the ID of the CCD itself.

Parameters:

dataReflsst.daf.persistence.ButlerDataRef: Butler data reference. The “CoaddId_bits” and “CoaddId” datasets are accessed. The data ID must have tract and patch keys.

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

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

Parameters:

namestr

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.

classmethod parseAndRun(args=None, config=None, log=None, doReturnResults=False)¶

Parse an argument list and run the command.

Parameters:

argslist, optional: List of command-line arguments; if None use sys.argv.
configlsst.pex.config.Config-type, optional: Config for task. If None use Task.ConfigClass.
loglogging.Logger-type, optional: Log. If None use the default log.
doReturnResultsbool, 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:

structlsst.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.

run(measCat, exposure, refCat, refWcs, exposureId=None)¶

Perform forced measurement on a single exposure.

Parameters:

measCatlsst.afw.table.SourceCatalog: The measurement catalog, based on the sources listed in the reference catalog.
exposurelsst.afw.image.Exposure: The measurement image upon which to perform forced detection.
refCatlsst.afw.table.SourceCatalog: The reference catalog of sources to measure.
refWcslsst.afw.image.SkyWcs: The WCS for the references.
exposureIdint: Optional unique exposureId used for random seed in measurement task.

Returns:

result~`lsst.pipe.base.Struct`

Structure with fields:

measCat: Catalog of forced measurement results (lsst.afw.table.SourceCatalog).

runDataRef(dataRef, psfCache=None)¶

Perform forced measurement on a single exposure.

Parameters:

dataReflsst.daf.persistence.ButlerDataRef: Passed to the references subtask to obtain the reference WCS, the getExposure method (implemented by derived classes) to read the measurment image, and the fetchReferences method to get the exposure and load the reference catalog (see :lsst-task`lsst.meas.base.references.CoaddSrcReferencesTask`). Refer to derived class documentation for details of the datasets and data ID keys which are used.
psfCacheint, optional: Size of PSF cache, or None. The size of the PSF cache can have a significant effect upon the runtime for complicated PSF models.

Notes

Sources are generated with generateMeasCat in the measurement subtask. These are passed to measurement’s run method, which fills the source catalog with the forced measurement results. The sources are then passed to the writeOutputs method (implemented by derived classes) which writes the outputs.

runQuantum(butlerQC, inputRefs, outputRefs)¶

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

Parameters:

butlerQCButlerQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefsInputQuantizedConnection: 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.
outputRefsOutputQuantizedConnection: 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.

timer(name: str, logLevel: int = 10) → Iterator[None]¶

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

Parameters:

namestr: Name of code being timed; data will be logged using item name: Start and End.
logLevel: A logging level constant.

Navigation

ForcedPhotCoaddTask¶