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

`applyOverrides`(config)	A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.
`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.
`fetchReferences`(dataRef, exposure)	Get sources that overlap the exposure.
`generateMeasCat`(exposureDataId, exposure, …)	Generate a measurement catalog for Gen3.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`getExposure`(dataRef)	Read input exposure for measurement.
`getExposureId`(dataRef)
`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`()	The schema catalogs that will be used by this task.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`makeField`(doc)	Make a `lsst.pex.config.ConfigurableField` for this task.
`makeIdFactory`(dataRef)	Create an object that generates globally unique source IDs.
`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.
`parseAndRun`([args, config, log, doReturnResults])	Parse an argument list and run the command.
`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.
`runDataRef`(dataRef[, psfCache])	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.
`writeConfig`(butler[, clobber, doBackup])	Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.
`writeMetadata`(dataRef)	Write the metadata produced from processing the data.
`writeOutput`(dataRef, sources)	Write forced source table
`writePackageVersions`(butler[, clobber, …])	Compare and write package versions.
`writeSchemas`(butler[, clobber, doBackup])	Write the schemas returned by `lsst.pipe.base.Task.getAllSchemaCatalogs`.

Attributes Documentation

canMultiprocess = True¶

dataPrefix = ''¶

Methods Documentation

classmethod applyOverrides(config)¶

A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.

Parameters:	config : instance of task’s `ConfigClass` Task configuration.

Notes

This is necessary in some cases because the camera-specific overrides may retarget subtasks, wiping out changes made in ConfigClass.setDefaults. See LSST Trac ticket #2282 for more discussion.

Warning

This is called by CmdLineTask.parseAndRun; other ways of constructing a config will not apply these overrides.

attachFootprints(sources, refCat, exposure, refWcs)¶

Attach footprints to blank sources prior to measurements.

Notes

Footprints 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 Footprints 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.

fetchReferences(dataRef, exposure)¶

Get sources that overlap the exposure.

Parameters:	dataRef : `lsst.daf.persistence.ButlerDataRef` Butler data reference corresponding to the image to be measured; should have `tract`, `patch`, and `filter` keys. exposure : `lsst.afw.image.Exposure` The image to be measured (used only to obtain a WCS and bounding box).
Returns:	referencs : `lsst.afw.table.SourceCatalog` Catalog of sources that overlap the exposure

Notes

The returned catalog is sorted by ID and guarantees that all included children have their parent included and that all Footprints are valid.

All work is delegated to the references subtask; see lsst.meas.base.references.CoaddSrcReferencesTask for information about the default behavior.

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

Generate a measurement catalog for Gen3.

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

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.

getExposure(dataRef)¶

Read input exposure for measurement.

Parameters:	dataRef : `lsst.daf.persistence.ButlerDataRef` Butler data reference.

getExposureId(dataRef)¶

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.

getSchemaCatalogs()¶

The schema catalogs that will be used by this task.

Returns:	schemaCatalogs : `dict` Dictionary mapping dataset type to schema catalog.

Notes

There is only one schema for each type of forced measurement. The dataset type for this measurement is defined in the mapper.

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.

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")

makeIdFactory(dataRef)¶

Create an object that generates globally unique source IDs.

Source IDs are created based on a per-CCD ID and the ID of the CCD itself.

Parameters:	dataRef : `lsst.daf.persistence.ButlerDataRef` Butler data reference. The `ccdExposureId_bits` and `ccdExposureId` datasets are accessed. The data ID must have the keys that correspond to `ccdExposureId`, which are generally the same as those that correspond to `calexp` (`visit`, `raft`, `sensor` for LSST data).

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

Filtering the reference catalog is currently handled by Gen2 specific methods. To function for Gen3, this method copies code segments to do the filtering and transformation. The majority of this code is based on the methods of lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader

classmethod parseAndRun(args=None, config=None, log=None, doReturnResults=False)¶

Parse an argument list and run the command.

Parameters:

args : list, optional: List of command-line arguments; if None use sys.argv.
config : lsst.pex.config.Config-type, optional: Config for task. If None use Task.ConfigClass.
log : logging.Logger-type, optional: Log. If None use the default log.
doReturnResults : bool, optional: If True, return the results of this task. Default is False. This is only intended for unit tests and similar use. It can easily exhaust memory (if the task returns enough data and you call it enough times) and it will fail when using multiprocessing if the returned data cannot be pickled.

Returns:

struct : lsst.pipe.base.Struct

Fields are:

argumentParser: the argument parser (lsst.pipe.base.ArgumentParser).
parsedCmd: the parsed command returned by the argument parser’s parse_args method (argparse.Namespace).
taskRunner: the task runner used to run the task (an instance of Task.RunnerClass).
resultList: results returned by the task runner’s run method, one entry per invocation (list). This will typically be a list of Struct, each containing at least an exitStatus integer (0 or 1); see Task.RunnerClass (TaskRunner by default) for more details.

Notes

Calling this method with no arguments specified is the standard way to run a command-line task from the command-line. For an example see pipe_tasks bin/makeSkyMap.py or almost any other file in that directory.

If one or more of the dataIds fails then this routine will exit (with a status giving the number of failed dataIds) rather than returning this struct; this behaviour can be overridden by specifying the --noExit command-line option.

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).

runDataRef(dataRef, psfCache=None)¶

Perform forced measurement on a single exposure.

Parameters:

dataRef : lsst.daf.persistence.ButlerDataRef: Passed to the references subtask to obtain the reference WCS, the getExposure method (implemented by derived classes) to read the measurment image, and the fetchReferences method to get the exposure and load the reference catalog (see :lsst-task`lsst.meas.base.references.CoaddSrcReferencesTask`). Refer to derived class documentation for details of the datasets and data ID keys which are used.
psfCache : int, optional: Size of PSF cache, or None. The size of the PSF cache can have a significant effect upon the runtime for complicated PSF models.

Notes

Sources are generated with generateMeasCat in the measurement subtask. These are passed to measurement’s run method, which fills the source catalog with the forced measurement results. The sources are then passed to the writeOutputs method (implemented by derived classes) which writes the outputs.

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

ForcedPhotCcdFromDataFrameTask¶