ProcessBrightStarsTask¶

class lsst.pipe.tasks.processBrightStars.ProcessBrightStarsTask(butler=None, initInputs=None, *args, **kwargs)¶

Bases: lsst.pipe.base.PipelineTask

The description of the parameters for this Task are detailed in PipelineTask.

Parameters:	initInputs : `Unknown` args Additional positional arguments. *kwargs Additional keyword arguments.

Notes

ProcessBrightStarsTask is used to extract, process, and store small image cut-outs (or “postage stamps”) around bright stars. It relies on three methods, called in succession:

extractStamps: Find bright stars within the exposure using a reference catalog and extract a stamp centered on each.
warpStamps: Shift and warp each stamp to remove optical distortions and sample all stars on the same pixel grid.
measureAndNormalize: Compute the flux of an object in an annulus and normalize it. This is required to normalize each bright star stamp as their central pixels are likely saturated and/or contain ghosts, and cannot be used.

Attributes Summary

canMultiprocess

Methods Summary

`applySkyCorr`(calexp, skyCorr)	Apply correction to the sky background level.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`extractStamps`(inputExposure[, refObjLoader])	Read position of bright stars within `inputExposure` from refCat and extract them.
`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`(inputExposure[, refObjLoader, dataId, …])	Identify bright stars within an exposure using a reference catalog, extract stamps around each, then preprocess them.
`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.
`warpStamps`(stamps, pixCenters)	Warps and shifts all given stamps so they are sampled on the same pixel grid and centered on the central pixel.

Attributes Documentation

canMultiprocess = True¶

Methods Documentation

applySkyCorr(calexp, skyCorr)¶

Apply correction to the sky background level.

Sky corrections can be generated with the ‘skyCorrection.py’ executable in pipe_drivers. Because the sky model used by that code extends over the entire focal plane, this can produce better sky subtraction. The calexp is updated in-place.

Parameters:	calexp : `lsst.afw.image.Exposure` or `lsst.afw.image.MaskedImage` Calibrated exposure. skyCorr : `lsst.afw.math.backgroundList.BackgroundList` or `None`, optional Full focal plane sky correction, obtained by running `lsst.pipe.drivers.skyCorrection.SkyCorrectionTask`.

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

extractStamps(inputExposure, refObjLoader=None)¶

Read position of bright stars within inputExposure from refCat and extract them.

Parameters:	inputExposure : `afwImage.exposure.exposure.ExposureF` The image from which bright star stamps should be extracted. refObjLoader : `lsst.meas.algorithms.ReferenceObjectLoader`, optional Loader to find objects within a reference catalog.
Returns:	result : `lsst.pipe.base.Struct` Results as a struct with attributes: `starIms` List of stamps (`list`). `pixCenters` List of corresponding coordinates to each star’s center, in pixels (`list`). `GMags` List of corresponding (Gaia) G magnitudes (`list`). `gaiaIds` Array of corresponding unique Gaia identifiers (`np.ndarray`).

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]¶

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(inputExposure, refObjLoader=None, dataId=None, skyCorr=None)¶

Identify bright stars within an exposure using a reference catalog, extract stamps around each, then preprocess them. The preprocessing steps are: shifting, warping and potentially rotating them to the same pixel grid; computing their annular flux and normalizing them.

Parameters:

inputExposure : afwImage.exposure.exposure.ExposureF: The image from which bright star stamps should be extracted.
refObjLoader : lsst.meas.algorithms.ReferenceObjectLoader, optional: Loader to find objects within a reference catalog.
dataId : dict or lsst.daf.butler.DataCoordinate: The dataId of the exposure (and detector) bright stars should be extracted from.
skyCorr : lsst.afw.math.backgroundList.BackgroundList or None,: optional

Full focal plane sky correction, obtained by running lsst.pipe.drivers.skyCorrection.SkyCorrectionTask.

Returns:

result : lsst.pipe.base.Struct

Results as a struct with attributes:

brightStarStamps: (bSS.BrightStarStamps)

runQuantum(butlerQC, inputRefs, outputRefs)¶

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

ProcessBrightStarsTask¶