GenerateDonutCatalogWcsTask¶

class lsst.ts.wep.task.GenerateDonutCatalogWcsTask(**kwargs)¶

Bases: PipelineTask

Create a WCS from boresight info and then use this with a reference catalog to select sources on the detectors for AOS.

Attributes Summary

canMultiprocess

Methods Summary

`donutCatalogToDataFrame`([donutCatalog, ...])	Reformat afwCatalog into a pandas dataframe sorted by flux with the brightest objects at the top.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`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.
`getRefObjLoader`(refCatalogList)	Create a `ReferenceObjectLoader` from available reference catalogs in the repository.
`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`(refCatalogs, exposure)	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
`runSelection`(refObjLoader, detector, wcs, ...)	Match the detector area to the reference catalog and then run the LSST DM reference selection task.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

donutCatalogToDataFrame(donutCatalog=None, blendCentersX=None, blendCentersY=None)¶

Reformat afwCatalog into a pandas dataframe sorted by flux with the brightest objects at the top.

Parameters:

donutCataloglsst.afw.table.SimpleCatalog or None, optional: lsst.afw.table.SimpleCatalog object returned by the ReferenceObjectLoader search over the detector footprint. If None then it will return an empty dataframe. (the default is None.)

Returns:

pandas.DataFrame: Complete catalog of reference sources in the pointing.

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

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

getRefObjLoader(refCatalogList)¶

Create a ReferenceObjectLoader from available reference catalogs in the repository.

Parameters:

refCatalogListlist: List of deferred butler references for the reference catalogs.

Returns:

lsst.meas.algorithms.ReferenceObjectsLoader: Object to conduct spatial searches through the reference catalogs

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.

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(refCatalogs: List[SimpleCatalog], exposure: Exposure) → 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.

runSelection(refObjLoader, detector, wcs, filterName)¶

Match the detector area to the reference catalog and then run the LSST DM reference selection task. For configuration parameters on the reference selector see lsst.meas.algorithms.ReferenceSourceSelectorConfig.

Parameters:

refObjLoadermeas.algorithms.ReferenceObjectLoader: Reference object loader to use in getting reference objects.
detectorlsst.afw.cameraGeom.Detector: Detector object from the camera.
wcslsst.afw.geom.SkyWcs: Wcs object defining the pixel to sky (and inverse) transform for the supplied bbox.
filterNamestr: Name of camera filter.

Returns:

referenceCataloglsst.afw.table.SimpleCatalog: Catalog containing reference objects inside the specified bounding box and with properties within the bounds set by the referenceSelector.

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

GenerateDonutCatalogWcsTask¶