ForcedPhotCoaddTask

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

Bases: lsst.pipe.base.PipelineTask, lsst.pipe.base.CmdLineTask

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

Parameters:
butler : lsst.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.

refSchema : lsst.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.
getExposureId(dataRef)
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) 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 = 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:
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.

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()

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:
dataRef : lsst.daf.persistence.ButlerDataRef

Butler data reference corresponding to the image to be measured; should have tract, patch, and filter keys.

exposure : lsst.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)

Generate a measurement catalog for Gen3.

Parameters:
exposureDataId : DataId

Butler dataId for this exposure.

exposure : lsst.afw.image.exposure.Exposure

Exposure to generate the catalog for.

refCat : lsst.afw.table.SourceCatalog

Catalog of shapes and positions at which to force photometry.

refCatInBand : lsst.afw.table.SourceCatalog

Catalog of shapes and position in the band forced photometry is currently being performed

refWcs : lsst.afw.image.SkyWcs

Reference world coordinate system.

idPackerName : str

Type of ID packer to construct from the registry.

Returns:
measCat : lsst.afw.table.SourceCatalog

Catalog of forced sources to measure.

expId : int

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()

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.

getExposure(dataRef)

Read input exposure on which measurement will be performed.

Parameters:
dataRef : lsst.daf.persistence.ButlerDataRef

Butler data reference.

getExposureId(dataRef)
getFullMetadata()

Get metadata for all tasks.

Returns:
metadata : lsst.daf.base.PropertySet

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

Notes

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

topLevelTaskName:subtaskName:subsubtaskName.itemName

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

getFullName()

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

Returns:
fullName : str

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

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

Get the name of the task.

Returns:
taskName : str

Name of the task.

See also

getFullName

getResourceConfig()

Return resource configuration for this task.

Returns:
Object of type `~config.ResourceConfig` or ``None`` if resource
configuration is not defined for this task.
getSchemaCatalogs()

The schema catalogs that will be used by this task.

Returns:
schemaCatalogs : dict

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()

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.

classmethod makeField(doc)

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

Parameters:
doc : str

Help text for the field.

Returns:
configurableField : lsst.pex.config.ConfigurableField

A ConfigurableField for this task.

Examples

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

Here is an example of use:

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

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:
dataRef : lsst.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, **keyArgs)

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

Parameters:
name : str

Brief name of the subtask.

keyArgs

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

  • “config”.
  • “parentTask”.

Notes

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

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.

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

Perform forced measurement on a single exposure.

Parameters:
measCat : lsst.afw.table.SourceCatalog

The measurement catalog, based on the sources listed in the reference catalog.

exposure : lsst.afw.image.Exposure

The measurement image upon which to perform forced detection.

refCat : lsst.afw.table.SourceCatalog

The reference catalog of sources to measure.

refWcs : lsst.afw.image.SkyWcs

The WCS for the references.

exposureId : int

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:
dataRef : lsst.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.

psfCache : int, 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:
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.

timer(name, logLevel=10)

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

Parameters:
name : str

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

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time
writeConfig(butler, clobber=False, doBackup=True)

Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to write the config. The config is written to dataset type CmdLineTask._getConfigName.

clobber : bool, optional

A boolean flag that controls what happens if a config already has been saved:

  • True: overwrite or rename the existing config, depending on doBackup.
  • False: raise TaskError if this config does not match the existing config.
doBackup : bool, optional

Set to True to backup the config files if clobbering.

writeMetadata(dataRef)

Write the metadata produced from processing the data.

Parameters:
dataRef

Butler data reference used to write the metadata. The metadata is written to dataset type CmdLineTask._getMetadataName.

writeOutput(dataRef, sources)

Write forced source table

Parameters:
dataRef : lsst.daf.persistence.ButlerDataRef

Butler data reference. The forced_src dataset (with self.dataPrefix prepended) is all that will be modified.

sources : lsst.afw.table.SourceCatalog

Catalog of sources to save.

writePackageVersions(butler, clobber=False, doBackup=True, dataset='packages')

Compare and write package versions.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to read/write the package versions.

clobber : bool, optional

A boolean flag that controls what happens if versions already have been saved:

  • True: overwrite or rename the existing version info, depending on doBackup.
  • False: raise TaskError if this version info does not match the existing.
doBackup : bool, optional

If True and clobbering, old package version files are backed up.

dataset : str, optional

Name of dataset to read/write.

Raises:
TaskError

Raised if there is a version mismatch with current and persisted lists of package versions.

Notes

Note that this operation is subject to a race condition.

writeSchemas(butler, clobber=False, doBackup=True)

Write the schemas returned by lsst.pipe.base.Task.getAllSchemaCatalogs.

Parameters:
butler : lsst.daf.persistence.Butler

Data butler used to write the schema. Each schema is written to the dataset type specified as the key in the dict returned by getAllSchemaCatalogs.

clobber : bool, optional

A boolean flag that controls what happens if a schema already has been saved:

  • True: overwrite or rename the existing schema, depending on doBackup.
  • False: raise TaskError if this schema does not match the existing schema.
doBackup : bool, optional

Set to True to backup the schema files if clobbering.

Notes

If clobber is False and an existing schema does not match a current schema, then some schemas may have been saved successfully and others may not, and there is no easy way to tell which is which.