FinalizeCharacterizationTask

class lsst.pipe.tasks.finalizeCharacterization.FinalizeCharacterizationTask(initInputs=None, **kwargs)

Bases: PipelineTask

Run final characterization on exposures.

Attributes Summary

canMultiprocess

Methods Summary

compute_psf_and_ap_corr_map(visit, detector, ...)	Compute psf model and aperture correction map for a single exposure.
concat_isolated_star_cats(band, ...)	Concatenate isolated star catalogs and make reserve selection.
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.
run(visit, band, isolated_star_cat_dict, ...)	Run the FinalizeCharacterizationTask.
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

compute_psf_and_ap_corr_map(visit, detector, exposure, src, isolated_source_table)

Compute psf model and aperture correction map for a single exposure.

Parameters:

visitint

Visit number (for logging).

detectorint

Detector number (for logging).

exposurelsst.afw.image.ExposureF

srclsst.afw.table.SourceCatalog

isolated_source_tablenp.ndarray

Returns:

psflsst.meas.algorithms.ImagePsf

PSF Model

ap_corr_maplsst.afw.image.ApCorrMap

Aperture correction map.

measured_srclsst.afw.table.SourceCatalog

Updated source catalog with measurements, flags and aperture corrections.

concat_isolated_star_cats(band, isolated_star_cat_dict, isolated_star_source_dict)

Concatenate isolated star catalogs and make reserve selection.

Parameters:

bandstr

Band name. Used to select reserved stars.

isolated_star_cat_dictdict

Per-tract dict of isolated star catalog handles.

isolated_star_source_dictdict

Per-tract dict of isolated star source catalog handles.

Returns:

isolated_tablenp.ndarray (N,)

Table of isolated stars, with indexes to isolated sources.

isolated_source_tablenp.ndarray (M,)

Table of isolated sources, with indexes to isolated stars.

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.

run(visit, band, isolated_star_cat_dict, isolated_star_source_dict, src_dict, calexp_dict)

Run the FinalizeCharacterizationTask.

Parameters:

visitint

Visit number. Used in the output catalogs.

bandstr

Band name. Used to select reserved stars.

isolated_star_cat_dictdict

Per-tract dict of isolated star catalog handles.

isolated_star_source_dictdict

Per-tract dict of isolated star source catalog handles.

src_dictdict

Per-detector dict of src catalog handles.

calexp_dictdict

Per-detector dict of calibrated exposure handles.

Returns:

structlsst.pipe.base.struct

Struct with outputs for persistence.

Raises:

NoWorkFound

Raised if the selector returns no good sources.

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