GbdesAstrometricFitTask

class lsst.drp.tasks.gbdesAstrometricFit.GbdesAstrometricFitTask(**kwargs)

Bases: PipelineTask

Calibrate the WCS across multiple visits of the same field using the GBDES package.

Attributes Summary

canMultiprocess

Methods Summary

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.
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.
make_yaml(inputVisitSummary[, inputFile, ...])	Make a YAML-type object that describes the parameters of the fit model.
run(inputCatalogRefs, inputVisitSummaries[, ...])	Run the WCS fit for a given set of visits
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.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True

Methods Documentation

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

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.

make_yaml(inputVisitSummary, inputFile=None, inputCameraModel=None)

Make a YAML-type object that describes the parameters of the fit model.

Parameters:

inputVisitSummarylsst.afw.table.ExposureCatalog

Catalog with per-detector summary information.

inputFilestr

Path to a file that contains a basic model.

inputCameraModeldict [str, np.ndarray], optional

Parameters to use for the device part of the model.

Returns:

inputYamlwcsfit.YAMLCollector

YAML object containing the model description.

inputDictdict [str, str]

Dictionary containing the model description.

run(inputCatalogRefs, inputVisitSummaries, instrumentName='', refEpoch=None, refObjectLoader=None, inputCameraModel=None, colorCatalog=None, inputCamera=None)

Run the WCS fit for a given set of visits

Parameters:

inputCatalogRefslist [DeferredDatasetHandle]

List of handles pointing to visit-level source tables.

inputVisitSummarieslist [lsst.afw.table.ExposureCatalog]

List of catalogs with per-detector summary information.

instrumentNamestr, optional

Name of the instrument used. This is only used for labelling.

refEpochfloat

Epoch of the reference objects in MJD.

refObjectLoaderinstance of

lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader Reference object loader instance.

inputCameraModeldict [str, np.ndarray], optional

Parameters to use for the device part of the model.

colorCataloglsst.afw.table.SimpleCatalog

Catalog containing object coordinates and magnitudes.

inputCameralsst.afw.cameraGeom.Camera, optional

Camera to be used as template when constructing new camera.

Returns:

resultlsst.pipe.base.Struct

outputWcsslist [lsst.afw.table.ExposureCatalog]

List of exposure catalogs (one per visit) with the WCS for each detector set by the new fitted WCS.

fitModelwcsfit.WCSFit

Model-fitting object with final model parameters.

outputCatalogpyarrow.Table

Catalog with fit residuals of all sources used.

starCatalogpyarrow.Table

Catalog with best-fit positions of the objects fit.

modelParamsdict

Parameters and covariance of the best-fit WCS model.

cameraModelParamsdict [str, np.ndarray]

Parameters of the device part of the model, in the format needed as input for future runs.

colorParamsdict [int, np.ndarray]

DCR parameters fit in RA and Dec directions for each visit.

cameralsst.afw.cameraGeom.Camera

Camera object constructed from the per-detector model.

runQuantum(butlerQC, inputRefs, outputRefs)

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.

See also

lsst.utils.timer.logInfo

Implementation function.

Examples

Creating a timer context:

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