TransformDiaSourceCatalogTask

class lsst.ap.association.TransformDiaSourceCatalogTask(initInputs, **kwargs)

Bases: TransformCatalogBaseTask

Transform a DiaSource catalog by calibrating and renaming columns to produce a table ready to insert into the Apdb.

Parameters:

initInputsdict

Must contain diaSourceSchema as the schema for the input catalog.

Attributes Summary

canMultiprocess
inputDataset
outputDataset

Methods Summary

addUnpackedFlagFunctors()	Add Column functor for each of the flags to the internal functor dictionary.
bitPackFlags(df)	Pack requested flag columns in inputRecord into single columns in outputRecord.
computeBBoxSizes(inputCatalog)	Compute the size of a square bbox that fully contains the detection footprint.
emptyMetadata()	Empty (clear) the metadata for this Task and all sub-Tasks.
getAnalysis(handles[, funcs, band])
getFullMetadata()	Get metadata for all tasks.
getFullName()	Get the task name as a hierarchical name including parent task names.
getFunctors()
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(diaSourceCat, diffIm, band, ccdVisitId)	Convert input catalog to ParquetTable/Pandas and run functors.
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.
transform(band, handles, funcs, dataId)

Attributes Documentation

canMultiprocess: ClassVar[bool] = True

inputDataset = 'deepDiff_diaSrc'

outputDataset = 'deepDiff_diaSrcTable'

Methods Documentation

addUnpackedFlagFunctors()

Add Column functor for each of the flags to the internal functor dictionary.

bitPackFlags(df)

Pack requested flag columns in inputRecord into single columns in outputRecord.

Parameters:

dfpandas.DataFrame

DataFrame to read bits from and pack them into.

computeBBoxSizes(inputCatalog)

Compute the size of a square bbox that fully contains the detection footprint.

Parameters:

inputCataloglsst.afw.table.SourceCatalog

Catalog containing detected footprints.

Returns:

outputBBoxSizesnp.ndarray, (N,)

Array of bbox sizes.

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

getAnalysis(handles, funcs=None, band=None)

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”.

getFunctors()

getName() → str

Get the name of the task.

Returns:

taskNamestr

Name of the task.

See also

getFullName

getTaskDict() → Dict[str, ReferenceType[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(diaSourceCat, diffIm, band, ccdVisitId, reliability=None)

Convert input catalog to ParquetTable/Pandas and run functors.

Additionally, add new columns for stripping information from the exposure and into the DiaSource catalog.

Parameters:

diaSourceCatlsst.afw.table.SourceCatalog

Catalog of sources measured on the difference image.

diffImlsst.afw.image.Exposure

Result of subtracting template and science images.

bandstr

Filter band of the science image.

ccdVisitIdint

Identifier for this detector+visit.

reliabilitylsst.afw.table.SourceCatalog

Reliability (e.g. real/bogus) scores, row-matched to diaSourceCat.

Returns:

resultslsst.pipe.base.Struct

Results struct with components.

• diaSourceTable : Catalog of DiaSources with calibrated values and renamed columns. (lsst.pipe.tasks.ParquetTable or pandas.DataFrame)

runQuantum(butlerQC, inputRefs, outputRefs)

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQCButlerQuantumContext

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.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time

transform(band, handles, funcs, dataId)