FgcmBuildStarsBaseTask¶

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

Bases: lsst.pipe.base.PipelineTask, abc.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.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`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`()	Get the schemas generated 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.
`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 = True¶

Methods Documentation

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

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

Compile all good star observations from visits in visitCat.

Parameters:	groupedHandles : `dict` [`list` [`lsst.daf.butler.DeferredDatasetHandle`]] Dataset handles, grouped by visit. visitCat : `afw.table.BaseCatalog` Catalog with visit data for FGCM sourceSchema : `lsst.afw.table.Schema` Schema for the input src catalogs. camera : `lsst.afw.cameraGeom.Camera` calibFluxApertureRadius : `float`, optional Aperture radius for calibration flux. inStarObsCat : `afw.table.BaseCatalog` Input observation catalog. If this is incomplete, observations will be appended from when it was cut off.
Returns:	fgcmStarObservations : `afw.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.

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.

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.

getFullMetadata() → lsst.pipe.base._task_metadata.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.

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”.

getName() → str¶

Get the name of the task.

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

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.

getTaskDict() → Dict[str, weakref.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.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField¶

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")

makeSubtask(name: str, **keyArgs) → 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”.

Notes

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

run(**kwargs) → 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:	struct : `Struct` 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: lsst.pipe.base.butlerQuantumContext.ButlerQuantumContext, inputRefs: lsst.pipe.base.connections.InputQuantizedConnection, outputRefs: lsst.pipe.base.connections.OutputQuantizedConnection) → None¶

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

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.

Navigation

FgcmBuildStarsBaseTask¶