GenerateDonutDirectDetectTask

class lsst.ts.wep.task.GenerateDonutDirectDetectTask(**kwargs)

Bases: PipelineTask

Generate donut template and convolve with the defocal image to detect sources on the detectors for AOS.

Attributes Summary

canMultiprocess

Methods Summary

emptyMetadata()	Empty (clear) the metadata for this Task and all sub-Tasks.
emptyTable()	Return empty donut table if no donuts got detected or selected.
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.
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(exposure, camera)	Run task algorithm on in-memory data.
runQuantum(butlerQC, inputRefs, outputRefs)	Do butler IO and transform to provide in memory objects for tasks run method.
timer(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.
updateDonutCatalog(donutCat, exposure)	Reorganize the content of donut catalog adding detector column, doing the transpose, and passing the exposure WCS boresight as coord_ra, coord_dec - these columns are required by EstimateZernikes, but not used explicitly downstream.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True

Methods Documentation

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

emptyTable()

Return empty donut table if no donuts got detected or selected.

Returns

astropy.table.QTable

An empty donut table with correct columns.

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 : Get the full name of the task.

getTaskDict() → dict[str, weakref.ReferenceType[lsst.pipe.base.task.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(exposure, camera)

Run task algorithm on in-memory data.

This method should be implemented in a subclass. This method will receive keyword-only 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 also has to override the runQuantum method.

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

Parameters

**kwargsAny

Arbitrary parameters accepted by subclasses.

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
    # connection 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: QuantumContext, inputRefs: InputQuantizedConnection, outputRefs: OutputQuantizedConnection) → None

Do butler IO and transform to provide in memory objects for tasks run method.

Parameters

butlerQCQuantumContext

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.

logLevelint

A logging level constant.

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time

See Also

lsst.utils.timer.logInfo : Implementation function.

updateDonutCatalog(donutCat, exposure)

Reorganize the content of donut catalog adding detector column, doing the transpose, and passing the exposure WCS boresight as coord_ra, coord_dec - these columns are required by EstimateZernikes, but not used explicitly downstream.

Parameters

donutCatastropy.table.QTable

The donut catalog from running DonutDetector, contains columns ‘y_center’, ‘x_center’

exposurelsst.afw.image.Exposure

Exposure with the donut images.

Returns

donutCatastropy.table.QTable

Donut catalog with reorganized content.