ProcessCcdWithVariableFakesTask¶

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

Bases: lsst.pipe.tasks.processCcdWithFakes.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

`addVariablity`(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.
`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.
`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)	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.

Attributes Documentation

canMultiprocess = True¶

Methods Documentation

addVariablity(fakeCat, band, exposure, photoCalib, exposureIdInfo)¶

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:	fakeCat : `pandas.DataFrame` Catalog of fakes to modify magnitudes of. band : `str` Current observing band to modify. exposure : `lsst.afw.image.ExposureF` Exposure fakes will be added to. photoCalib : `lsst.afw.image.PhotoCalib` Photometric calibration object of `exposure`. exposureIdInfo : `lsst.obs.base.ExposureIdInfo` Exposure id information and metadata.
Returns:	dataFrame : `pandas.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:	fakeCats : `list` of `lst.daf.butler.DeferredDatasetHandle` Set of fake cats to concatenate. skyMap : `lsst.skymap.SkyMap` SkyMap defining the geometry of the tracts and patches.
Returns:	combinedFakeCat : `pandas.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:	calibCat : `lsst.afw.table.SourceCatalog` Catalog from which to copy fields. sourceCat : `lsst.afw.table.SourceCatalog` Catalog to which to copy fields. fieldsToCopy : `lsst.pex.config.listField.List` Fields to copy from calibCat to SoourceCat.
Returns:	newCat : `lsst.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.

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.

getVisitMatchedFakeCat(fakeCat, exposure)¶

Trim the fakeCat to select particular visit

Parameters:	fakeCat : `pandas.core.frame.DataFrame` The catalog of fake sources to add to the exposure exposure : `lsst.afw.image.exposure.exposure.ExposureF` The exposure to add the fake sources to
Returns:	movingFakeCat : `pandas.DataFrame` All fakes that belong to the visit

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

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

Parameters:

fakeCat : pandas.core.frame.DataFrame: The catalog of fake sources to add to the exposure
exposure : lsst.afw.image.exposure.exposure.ExposureF: The exposure to add the fake sources to
skyMap : lsst.skymap.SkyMap: SkyMap defining the tracts and patches the fakes are stored over.
wcs : lsst.afw.geom.SkyWcs: WCS to use to add fake sources
photoCalib : lsst.afw.image.photoCalib.PhotoCalib: Photometric calibration to be used to calibrate the fake sources
exposureIdInfo : lsst.obs.base.ExposureIdInfo
icSourceCat : lsst.afw.table.SourceCatalog: Default : None Catalog to take the information about which sources were used for calibration from.
sfdSourceCat : lsst.afw.table.SourceCatalog: Default : None Catalog produced by singleFrameDriver, needed to copy some calibration flags from.

Returns:

resultStruct : lsst.pipe.base.Struct

Results Strcut 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.

If exposureIdInfo is not provided then the SourceCatalog IDs will not be globally unique.

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

ProcessCcdWithVariableFakesTask¶