ForcedPhotCoaddTask¶

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

Bases: lsst.meas.base.ForcedPhotImageTask

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

Notes

In addition to the run method, ForcedPhotCcdTask overrides several methods of ForcedPhotImageTask to specialize it for coadd processing, including makeIdFactory and fetchReferences. None of these should be called directly by the user, though it may be useful to override them further in subclasses.

Attributes Summary

`canMultiprocess`
`dataPrefix`

Methods Summary

`adaptArgsAndRun`(inputData, inputDataIds, …)	Run task algorithm on in-memory data.
`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.
`getDatasetTypes`(config, configClass)	Return dataset type descriptors defined in task configuration.
`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.
`getInitInputDatasetTypes`(config)	Return dataset type descriptors that can be used to retrieve the `initInputs` constructor argument.
`getInitOutputDatasetTypes`(config)	Return dataset type descriptors that can be used to write the objects returned by `getOutputDatasets`.
`getInitOutputDatasets`()	Return persistable outputs that are available immediately after the task has been constructed.
`getInputDatasetTypes`(config)	Return input dataset type descriptors for this task.
`getName`()	Get the name of the task.
`getOutputDatasetTypes`(config)	Return output dataset type descriptors for this task.
`getPerDatasetTypeDimensions`(config)	Return any Dimensions that are permitted to have different values for different DatasetTypes within the same quantum.
`getPrerequisiteDatasetTypes`(config)	Return the local names of input dataset types that should be assumed to exist instead of constraining what data to process with this 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`(quantum, butler)	Execute PipelineTask algorithm on single quantum of data.
`saveStruct`(struct, outputDataRefs, butler)	Save data in butler.
`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

adaptArgsAndRun(inputData, inputDataIds, outputDataIds, butler)¶

Run task algorithm on in-memory data.

This method is called by runQuantum to operate on input in-memory data and produce coressponding output in-memory data. It receives arguments which are dictionaries with input data and input/output DataIds. Many simple tasks do not need to know DataIds so default implementation of this method calls run method passing input data objects as keyword arguments. Most simple tasks will implement run method, more complex tasks that need to know about output DataIds will override this method instead.

All three arguments to this method are dictionaries with keys equal to the name of the configuration fields for dataset type. If dataset type is configured with scalar fiels set to True then it is expected that only one dataset appears on input or output for that dataset type and dictionary value will be a single data object or DataId. Otherwise if scalar is False (default) then value will be a list (even if only one item is in the list).

The method returns Struct instance with attributes matching the configuration fields for output dataset types. Values stored in returned struct are single object if scalar is True or list of objects otherwise. If tasks produces more than one object for some dataset type then data objects returned in struct must match in count and order corresponding DataIds in outputDataIds.

Parameters:

inputData : dict: Dictionary whose keys are the names of the configuration fields describing input dataset types and values are Python-domain data objects (or lists of objects) retrieved from data butler.
inputDataIds : dict: Dictionary whose keys are the names of the configuration fields describing input dataset types and values are DataIds (or lists of DataIds) that task consumes for corresponding dataset type. DataIds are guaranteed to match data objects in inputData
outputDataIds : dict: Dictionary whose keys are the names of the configuration fields describing output dataset types and values are DataIds (or lists of DataIds) that task is to produce for corresponding dataset type.

Returns:

struct : Struct: Standard convention is that this method should return Struct instance containing all output data. Struct attribute names should correspond to the names of the configuration fields describing task output dataset types. If something different is returned then saveStruct method has to be re-implemented accordingly.

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, refWcs, idPackerName, butler)¶

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.
refWcs : lsst.afw.image.SkyWcs: Reference world coordinate system.
idPackerName : str: Type of ID packer to construct from the registry.
butler : lsst.daf.persistence.butler.Butler: Butler to use to construct id packer.

Returns:

measCat : lsst.afw.table.SourceCatalog: Catalog of forced sources to measure.

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.

classmethod getDatasetTypes(config, configClass)¶

Return dataset type descriptors defined in task configuration.

This method can be used by other methods that need to extract dataset types from task configuration (e.g. getInputDatasetTypes or sub-class methods).

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration. configClass : `type` Class of the configuration object which defines dataset type.
Returns:	Dictionary where key is the name (arbitrary) of the output dataset and value is the `DatasetTypeDescriptor` instance. Default implementation uses configuration field name as dictionary key. Returns empty dict if configuration has no fields with the specified ``configClass``.

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”.

classmethod getInitInputDatasetTypes(config)¶

Return dataset type descriptors that can be used to retrieve the initInputs constructor argument.

Datasets used in initialization may not be associated with any Dimension (i.e. their data IDs must be empty dictionaries).

Default implementation finds all fields of type InitInputInputDatasetConfig in configuration (non-recursively) and uses them for constructing DatasetTypeDescriptor instances. The names of these fields are used as keys in returned dictionary. Subclasses can override this behavior.

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration.
Returns:	Dictionary where key is the name (arbitrary) of the input dataset and value is the `DatasetTypeDescriptor` instance. Default implementation uses configuration field name as dictionary key. When the task requires no initialization inputs, should return an empty dict.

classmethod getInitOutputDatasetTypes(config)¶

Return dataset type descriptors that can be used to write the objects returned by getOutputDatasets.

Datasets used in initialization may not be associated with any Dimension (i.e. their data IDs must be empty dictionaries).

Default implementation finds all fields of type InitOutputDatasetConfig in configuration (non-recursively) and uses them for constructing DatasetTypeDescriptor instances. The names of these fields are used as keys in returned dictionary. Subclasses can override this behavior.

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration.
Returns:	Dictionary where key is the name (arbitrary) of the output dataset and value is the `DatasetTypeDescriptor` instance. Default implementation uses configuration field name as dictionary key. When the task produces no initialization outputs, should return an empty dict.

getInitOutputDatasets()¶

Return persistable outputs that are available immediately after the task has been constructed.

Subclasses that operate on catalogs should override this method to return the schema(s) of the catalog(s) they produce.

It is not necessary to return the PipelineTask’s configuration or other provenance information in order for it to be persisted; that is the responsibility of the execution system.

Returns:	datasets : `dict` Dictionary with keys that match those of the dict returned by `getInitOutputDatasetTypes` values that can be written by calling `Butler.put` with those DatasetTypes and no data IDs. An empty `dict` should be returned by tasks that produce no initialization outputs.

classmethod getInputDatasetTypes(config)¶

Return input dataset type descriptors for this task.

Default implementation finds all fields of type InputDatasetConfig in configuration (non-recursively) and uses them for constructing DatasetTypeDescriptor instances. The names of these fields are used as keys in returned dictionary. Subclasses can override this behavior.

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration.
Returns:	Dictionary where key is the name (arbitrary) of the input dataset and value is the `DatasetTypeDescriptor` instance. Default implementation uses configuration field name as dictionary key.

getName()¶

Get the name of the task.

Returns:	taskName : `str` Name of the task.

See also

getFullName

classmethod getOutputDatasetTypes(config)¶

Return output dataset type descriptors for this task.

Default implementation finds all fields of type OutputDatasetConfig in configuration (non-recursively) and uses them for constructing DatasetTypeDescriptor instances. The keys of these fields are used as keys in returned dictionary. Subclasses can override this behavior.

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration.
Returns:	Dictionary where key is the name (arbitrary) of the output dataset and value is the `DatasetTypeDescriptor` instance. Default implementation uses configuration field name as dictionary key.

classmethod getPerDatasetTypeDimensions(config)¶

Return any Dimensions that are permitted to have different values for different DatasetTypes within the same quantum.

Parameters:	config : `Config` Configuration for this task.
Returns:	dimensions : `Set` of `Dimension` or `str` The dimensions or names thereof that should be considered per-DatasetType.

Notes

Any Dimension declared to be per-DatasetType by a PipelineTask must also be declared to be per-DatasetType by other PipelineTasks in the same Pipeline.

The classic example of a per-DatasetType dimension is the CalibrationLabel dimension that maps to a validity range for master calibrations. When running Instrument Signature Removal, one does not care that different dataset types like flat, bias, and dark have different validity ranges, as long as those validity ranges all overlap the relevant observation.

classmethod getPrerequisiteDatasetTypes(config)¶

Return the local names of input dataset types that should be assumed to exist instead of constraining what data to process with this task.

Usually, when running a PipelineTask, the presence of input datasets constrains the processing to be done (as defined by the QuantumGraph generated during “preflight”). “Prerequisites” are special input datasets that do not constrain that graph, but instead cause a hard failure when missing. Calibration products and reference catalogs are examples of dataset types that should usually be marked as prerequisites.

Parameters:	config : `Config` Configuration for this task. Typically datasets are defined in a task configuration.
Returns:	prerequisite : `Set` of `str` The keys in the dictionary returned by `getInputDatasetTypes` that represent dataset types that should be considered prerequisites. Names returned here that are not keys in that dictionary are ignored; that way, if a config option removes an input dataset type only `getInputDatasetTypes` needs to be updated.

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("a brief description of what this task does")

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 pex_config 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 : lsst.log.Log-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.
parsedCmd: the parsed command returned by the argument parser’s lsst.pipe.base.ArgumentParser.parse_args method.
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.

This will typically be a list of None unless doReturnResults is True; see Task.RunnerClass (TaskRunner by default) for more information.

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(quantum, butler)¶

Execute PipelineTask algorithm on single quantum of data.

Typical implementation of this method will use inputs from quantum to retrieve Python-domain objects from data butler and call adaptArgsAndRun method on that data. On return from adaptArgsAndRun this method will extract data from returned Struct instance and save that data to butler.

The Struct returned from adaptArgsAndRun is expected to contain data attributes with the names equal to the names of the configuration fields defining output dataset types. The values of the data attributes must be data objects corresponding to the DataIds of output dataset types. All data objects will be saved in butler using DataRefs from Quantum’s output dictionary.

This method does not return anything to the caller, on errors corresponding exception is raised.

Parameters:	quantum : `Quantum` Object describing input and output corresponding to this invocation of PipelineTask instance. butler : object Data butler instance.
Raises:	`ScalarError` if a dataset type is configured as scalar but receives multiple DataIds in `quantum`. Any exceptions that happen in data butler or in `adaptArgsAndRun` method.

saveStruct(struct, outputDataRefs, butler)¶

Save data in butler.

Convention is that struct returned from run() method has data field(s) with the same names as the config fields defining output DatasetTypes. Subclasses may override this method to implement different convention for Struct content or in case any post-processing of data may be needed.

Parameters:	struct : `Struct` Data produced by the task packed into `Struct` instance outputDataRefs : `dict` Dictionary whose keys are the names of the configuration fields describing output dataset types and values are lists of DataRefs. DataRefs must match corresponding data objects in `struct` in number and order. butler : object Data butler instance.

timer(name, logLevel=10000)¶

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 `lsst.log` level constant.

Navigation

ForcedPhotCoaddTask¶