MatchVariableFakesTask¶

class lsst.pipe.tasks.matchFakes.MatchVariableFakesTask(*, config: Optional[PipelineTaskConfig] = None, log: Optional[Union[logging.Logger, LsstLogAdapter]] = None, initInputs: Optional[Dict[str, Any]] = None, **kwargs)¶

Bases: lsst.pipe.tasks.matchFakes.MatchFakesTask

Match injected fakes to their detected sources in the catalog and compute their expected brightness in a difference image assuming perfect subtraction.

This task is generally for injected sources that cannot be easily identified by their footprints such as in the case of detector sources post image differencing.

Attributes Summary

canMultiprocess

Methods Summary

`composeFakeCat`(fakeCats, skyMap)	Concatenate the fakeCats from tracts that may cover the exposure.
`computeExpectedDiffMag`(fakeCat, …)	Compute the magnitude expected in the difference image for this detector/visit.
`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, ccdVisitFakeMagnitudes, …)	Match fakes to detected diaSources within a difference image bound.
`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

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.

computeExpectedDiffMag(fakeCat, ccdVisitFakeMagnitudes, band)¶

Compute the magnitude expected in the difference image for this detector/visit. Modify fakeCat in place.

Negative magnitudes indicate that the source should be detected as a negative source.

Parameters:	fakeCat : `pandas.DataFrame` Catalog of fake sources. ccdVisitFakeMagnitudes : `pandas.DataFrame` Magnitudes for variable sources in this specific ccdVisit. band : `str` Band that this ccdVisit was observed in.

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, ccdVisitFakeMagnitudes, skyMap, diffIm, associatedDiaSources, band)¶

Match fakes to detected diaSources within a difference image bound.

Parameters:	fakeCat : `pandas.DataFrame` Catalog of fakes to match to detected diaSources. diffIm : `lsst.afw.image.Exposure` Difference image where `associatedDiaSources` were detected in. associatedDiaSources : `pandas.DataFrame` Catalog of difference image sources detected in `diffIm`.
Returns:	result : `lsst.pipe.base.Struct` Results struct with components. `matchedDiaSources` : Fakes matched to input diaSources. Has length of `fakeCat`. (`pandas.DataFrame`)

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

MatchVariableFakesTask¶