ProcessCcdWithVariableFakesTask

class lsst.pipe.tasks.processCcdWithFakes.ProcessCcdWithVariableFakesTask(schema=None, **kwargs)

Bases: ProcessCcdWithFakesTask

As ProcessCcdWithFakes except add variablity to the fakes catalog magnitude in the observed band for this ccdVisit.

Additionally, write out the modified magnitudes to the Butler.

Attributes Summary

canMultiprocess

Methods Summary

addVariability(fakeCat, band, exposure, ...)	Add scatter to the fake catalog visit magnitudes.
composeFakeCat(fakeCats, skyMap)	Concatenate the fakeCats from tracts that may cover the exposure.
copyCalibrationFields(calibCat, sourceCat, ...)	Match sources in calibCat and sourceCat and copy the specified fields
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.
getVisitMatchedFakeCat(fakeCat, exposure)	Trim the fakeCat to select particular visit
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(fakeCats, exposure, skyMap[, wcs, ...])	Add fake sources to a calexp and then run detection, deblending and measurement.
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

addVariability(fakeCat, band, exposure, photoCalib, rngSeed)

Add scatter to the fake catalog visit magnitudes.

Currently just adds a simple Gaussian scatter around the static fake magnitude. This function could be modified to return any number of fake variability.

Parameters:

fakeCatpandas.DataFrame

Catalog of fakes to modify magnitudes of.

bandstr

Current observing band to modify.

exposurelsst.afw.image.ExposureF

Exposure fakes will be added to.

photoCaliblsst.afw.image.PhotoCalib

Photometric calibration object of exposure.

rngSeedint

Random number generator seed.

Returns:

dataFramepandas.DataFrame

DataFrame containing the values of the magnitudes to that will be inserted into this ccdVisit.

composeFakeCat(fakeCats, skyMap)

Concatenate the fakeCats from tracts that may cover the exposure.

Parameters:

fakeCatslist of lsst.daf.butler.DeferredDatasetHandle

Set of fake cats to concatenate.

skyMaplsst.skymap.SkyMap

SkyMap defining the geometry of the tracts and patches.

Returns:

combinedFakeCatpandas.DataFrame

All fakes that cover the inner polygon of the tracts in this quantum.

copyCalibrationFields(calibCat, sourceCat, fieldsToCopy)

Match sources in calibCat and sourceCat and copy the specified fields

Parameters:

calibCatlsst.afw.table.SourceCatalog

Catalog from which to copy fields.

sourceCatlsst.afw.table.SourceCatalog

Catalog to which to copy fields.

fieldsToCopylsst.pex.config.listField.List

Fields to copy from calibCat to SoourceCat.

Returns:

newCatlsst.afw.table.SourceCatalog

Catalog which includes the copied fields.

The fields copied are those specified by fieldsToCopy that actually exist

in the schema of calibCat.

This version was based on and adapted from the one in calibrateTask.

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.

getVisitMatchedFakeCat(fakeCat, exposure)

Trim the fakeCat to select particular visit

Parameters:

fakeCatpandas.core.frame.DataFrame

The catalog of fake sources to add to the exposure

exposurelsst.afw.image.exposure.exposure.ExposureF

The exposure to add the fake sources to

Returns:

movingFakeCatpandas.DataFrame

All fakes that belong to the visit

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(fakeCats, exposure, skyMap, wcs=None, photoCalib=None, icSourceCat=None, sfdSourceCat=None, idGenerator=None)

Add fake sources to a calexp and then run detection, deblending and measurement.

Parameters:

fakeCatpandas.core.frame.DataFrame

The catalog of fake sources to add to the exposure.

exposurelsst.afw.image.exposure.exposure.ExposureF

The exposure to add the fake sources to.

skyMaplsst.skymap.SkyMap

SkyMap defining the tracts and patches the fakes are stored over.

wcslsst.afw.geom.SkyWcs, optional

WCS to use to add fake sources.

photoCaliblsst.afw.image.photoCalib.PhotoCalib, optional

Photometric calibration to be used to calibrate the fake sources.

icSourceCatlsst.afw.table.SourceCatalog, optional

Catalog to take the information about which sources were used for calibration from.

sfdSourceCatlsst.afw.table.SourceCatalog, optional

Catalog produced by singleFrameDriver, needed to copy some calibration flags from.

idGeneratorlsst.meas.base.IdGenerator, optional

Object that generates Source IDs and random seeds.

Returns:

resultStructlsst.pipe.base.struct.Struct

Results struct containing:

• outputExposure : Exposure with added fakes (lsst.afw.image.exposure.exposure.ExposureF)

• outputCat : Catalog with detected fakes (lsst.afw.table.source.source.SourceCatalog)

• ccdVisitFakeMagnitudes : Magnitudes that these fakes were inserted with after being scattered (pandas.DataFrame)

Notes

Adds pixel coordinates for each source to the fakeCat and removes objects with bulge or disk half light radius = 0 (if config.cleanCat = True). These columns are called x and y and are in pixels.

Adds the Fake mask plane to the exposure which is then set by addFakeSources to mark where fake sources have been added. Uses the information in the fakeCat to make fake galaxies (using galsim) and fake stars, using the PSF models from the PSF information for the calexp. These are then added to the calexp and the calexp with fakes included returned.

The galsim galaxies are made using a double sersic profile, one for the bulge and one for the disk, this is then convolved with the PSF at that point.

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