ForcedPhotCcdFromDataFrameTask

class lsst.meas.base.ForcedPhotCcdFromDataFrameTask(butler=None, refSchema=None, initInputs=None, **kwds)

Bases: lsst.meas.base.ForcedPhotCcdTask

Force Photometry on a per-detector exposure with coords from a DataFrame

Uses input from a DataFrame instead of SourceCatalog like the base class ForcedPhotCcd does. Writes out a SourceCatalog so that the downstream WriteForcedSourceTableTask can be reused with output from this Task.

Attributes Summary

canMultiprocess
dataPrefix

Methods Summary

attachFootprints(sources, refCat, exposure, …)	Attach footprints to blank sources prior to measurements.
df2RefCat(dfList, exposureBBox, exposureWcs)	Convert list of DataFrames to reference catalog
df2SourceCat(df)	Create minimal schema SourceCatalog from a pandas DataFrame.
emptyMetadata()	Empty (clear) the metadata for this Task and all sub-Tasks.
generateMeasCat(exposureDataId, exposure, …)	Generate a measurement catalog.
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.
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.
mergeAndFilterReferences(exposure, refCats, …)	Filter reference catalog so that all sources are within the boundaries of the exposure.
prepareCalibratedExposure(exposure[, …])	Prepare a calibrated exposure and apply external calibrations and sky corrections if so configured.
run(measCat, exposure, refCat, refWcs[, …])	Perform forced measurement on a single exposure.
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

dataPrefix = ''

Methods Documentation

attachFootprints(sources, refCat, exposure, refWcs)

Attach footprints to blank sources prior to measurements.

Notes

Footprint objects for forced photometry must be in the pixel coordinate system of the image being measured, while the actual detections may start out in a different coordinate system.

Subclasses of this class may implement this method to define how those Footprint objects should be generated.

This default implementation transforms depends on the footprintSource configuration parameter.

df2RefCat(dfList, exposureBBox, exposureWcs)

Convert list of DataFrames to reference catalog

Concatenate list of DataFrames presumably from multiple patches and downselect rows that overlap the exposureBBox using the exposureWcs.

Parameters:

dfList : list of pandas.DataFrame

Each element containst diaObjects with ra/decl position in degrees Columns ‘diaObjectId’, ‘ra’, ‘decl’ are expected

exposureBBox : lsst.geom.Box2I

Bounding box on which to select rows that overlap

exposureWcs : lsst.afw.geom.SkyWcs

World coordinate system to convert sky coords in ref cat to pixel coords with which to compare with exposureBBox

Returns:

refCat : lsst.afw.table.SourceTable

Source Catalog with minimal schema that overlaps exposureBBox

df2SourceCat(df)

Create minimal schema SourceCatalog from a pandas DataFrame.

The forced measurement subtask expects this as input.

Parameters:

df : pandas.DataFrame

DiaObjects with locations and ids.

Returns:

outputCatalog : lsst.afw.table.SourceTable

Output catalog with minimal schema.

emptyMetadata() → None

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

generateMeasCat(exposureDataId, exposure, refCat, refWcs, idPackerName)

Generate a measurement catalog.

Parameters:

exposureDataId : DataId

Butler dataId for this exposure.

exposure : lsst.afw.image.exposure.Exposure

Exposure to generate the catalog for.

refCat : lsst.afw.table.SourceCatalog

Catalog of shapes and positions at which to force photometry.

refWcs : lsst.afw.image.SkyWcs

Reference world coordinate system. This parameter is not currently used.

idPackerName : str

Type of ID packer to construct from the registry.

Returns:

measCat : lsst.afw.table.SourceCatalog

Catalog of forced sources to measure.

expId : int

Unique binary id associated with the input exposure

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

getFullName

getResourceConfig() → Optional[ResourceConfig]

Return resource configuration for this task.

Returns:

Object of type ResourceConfig or None if resource

configuration is not defined for this task.

getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

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.

mergeAndFilterReferences(exposure, refCats, refWcs)

Filter reference catalog so that all sources are within the boundaries of the exposure.

Parameters:

exposure : lsst.afw.image.exposure.Exposure

Exposure to generate the catalog for.

refCats : sequence of lsst.daf.butler.DeferredDatasetHandle

Handles for catalogs of shapes and positions at which to force photometry.

refWcs : lsst.afw.image.SkyWcs

Reference world coordinate system.

Returns:

refSources : lsst.afw.table.SourceCatalog

Filtered catalog of forced sources to measure.

Notes

The majority of this code is based on the methods of lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader

prepareCalibratedExposure(exposure, skyCorr=None, externalSkyWcsCatalog=None, externalPhotoCalibCatalog=None, finalizedPsfApCorrCatalog=None)

Prepare a calibrated exposure and apply external calibrations and sky corrections if so configured.

Parameters:

exposure : lsst.afw.image.exposure.Exposure

Input exposure to adjust calibrations.

skyCorr : lsst.afw.math.backgroundList, optional

Sky correction frame to apply if doApplySkyCorr=True.

externalSkyWcsCatalog : lsst.afw.table.ExposureCatalog, optional

Exposure catalog with external skyWcs to be applied if config.doApplyExternalSkyWcs=True. Catalog uses the detector id for the catalog id, sorted on id for fast lookup.

externalPhotoCalibCatalog : lsst.afw.table.ExposureCatalog, optional

Exposure catalog with external photoCalib to be applied if config.doApplyExternalPhotoCalib=True. Catalog uses the detector id for the catalog id, sorted on id for fast lookup.

finalizedPsfApCorrCatalog : lsst.afw.table.ExposureCatalog, optional

Exposure catalog with finalized psf models and aperture correction maps to be applied if config.doApplyFinalizedPsf=True. Catalog uses the detector id for the catalog id, sorted on id for fast lookup.

Returns:

exposure : lsst.afw.image.exposure.Exposure

Exposure with adjusted calibrations.

run(measCat, exposure, refCat, refWcs, exposureId=None)

Perform forced measurement on a single exposure.

Parameters:

measCat : lsst.afw.table.SourceCatalog

The measurement catalog, based on the sources listed in the reference catalog.

exposure : lsst.afw.image.Exposure

The measurement image upon which to perform forced detection.

refCat : lsst.afw.table.SourceCatalog

The reference catalog of sources to measure.

refWcs : lsst.afw.image.SkyWcs

The WCS for the references.

exposureId : int

Optional unique exposureId used for random seed in measurement task.

Returns:

result : lsst.pipe.base.Struct

Structure with fields:

measCat

Catalog of forced measurement results (lsst.afw.table.SourceCatalog).

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.

See also

timer.logInfo

Examples

Creating a timer context:

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