FgcmBuildStarsBaseTask¶

class lsst.fgcmcal.FgcmBuildStarsBaseTask(initInputs=None, **kwargs)¶

Bases: PipelineTask, ABC

Base task to build stars for FGCM global calibration

Attributes Summary

canMultiprocess

Methods Summary

`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`fgcmMakeAllStarObservations`(groupedHandles, ...)	Compile all good star observations from visits in visitCat.
`fgcmMakeVisitCatalog`(camera, groupedHandles)	Make a visit catalog with all the keys from each visit
`fgcmMatchStars`(visitCat, obsCat[, lutHandle])	Use FGCM code to match observations into unique stars.
`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.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`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.
`run`(**kwargs)	Run task algorithm on in-memory data.
`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.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

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

abstract fgcmMakeAllStarObservations(groupedHandles, visitCat, sourceSchema, camera, calibFluxApertureRadius=None)¶

Compile all good star observations from visits in visitCat.

Parameters:

groupedHandlesdict [list [lsst.daf.butler.DeferredDatasetHandle]]: Dataset handles, grouped by visit.
visitCatafw.table.BaseCatalog: Catalog with visit data for FGCM
sourceSchemalsst.afw.table.Schema: Schema for the input src catalogs.
cameralsst.afw.cameraGeom.Camera
calibFluxApertureRadiusfloat, optional: Aperture radius for calibration flux.
inStarObsCatafw.table.BaseCatalog: Input observation catalog. If this is incomplete, observations will be appended from when it was cut off.

Returns:

fgcmStarObservationsafw.table.BaseCatalog: Full catalog of good observations.

Raises:

RuntimeError: Raised if doSubtractLocalBackground is True and: calibFluxApertureRadius is not set.

fgcmMakeVisitCatalog(camera, groupedHandles, bkgHandleDict=None)¶

Make a visit catalog with all the keys from each visit

Parameters:

camera: `lsst.afw.cameraGeom.Camera`: Camera from the butler
groupedHandles: `dict` [`list` [`lsst.daf.butler.DeferredDatasetHandle`]]: Dataset handles, grouped by visit.
bkgHandleDict: `dict`, optional: Dictionary of lsst.daf.butler.DeferredDatasetHandle for background info.

Returns:

visitCat: afw.table.BaseCatalog

fgcmMatchStars(visitCat, obsCat, lutHandle=None)¶

Use FGCM code to match observations into unique stars.

Parameters:

visitCat: `afw.table.BaseCatalog`: Catalog with visit data for fgcm
obsCat: `afw.table.BaseCatalog`: Full catalog of star observations for fgcm
lutHandle: `lsst.daf.butler.DeferredDatasetHandle`, optional: Data reference to fgcm look-up table (used if matching reference stars).

Returns:

fgcmStarIdCat: afw.table.BaseCatalog: Catalog of unique star identifiers and index keys
fgcmStarIndicesCat: afwTable.BaseCatalog: Catalog of unique star indices
fgcmRefCat: afw.table.BaseCatalog: Catalog of matched reference stars. Will be None if config.doReferenceMatches is False.

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() → ResourceConfig | None¶

Return resource configuration for this task.

Returns:

Object of type ResourceConfig or None if resource
configuration is not defined for this task.

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")

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.

run(**kwargs: Any) → Struct¶

Run task algorithm on in-memory data.

This method should be implemented in a subclass. This method will receive keyword arguments whose names will be the same as names of connection fields describing input dataset types. Argument values will be data objects retrieved from data butler. If a dataset type is configured with multiple field set to True then the argument value will be a list of objects, otherwise it will be a single object.

If the task needs to know its input or output DataIds then it has to override runQuantum method instead.

This method should return a Struct whose attributes share the same name as the connection fields describing output dataset types.

Returns:

structStruct: Struct with attribute names corresponding to output connection fields

Examples

Typical implementation of this method may look like:

def run(self, input, calib):
    # "input", "calib", and "output" are the names of the config
    # fields

    # Assuming that input/calib datasets are `scalar` they are
    # simple objects, do something with inputs and calibs, produce
    # output image.
    image = self.makeImage(input, calib)

    # If output dataset is `scalar` then return object, not list
    return Struct(output=image)

runQuantum(butlerQC: ButlerQuantumContext, inputRefs: InputQuantizedConnection, outputRefs: OutputQuantizedConnection) → None¶

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

FgcmBuildStarsBaseTask¶