CharacterizeImageTask

class lsst.pipe.tasks.characterizeImage.CharacterizeImageTask(schema=None, **kwargs)

Bases: PipelineTask

Measure bright sources and use this to estimate background and PSF of an exposure.

Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask): - detect and measure bright sources - repair cosmic rays - detect and mask streaks - measure and subtract background - measure PSF

Parameters:
schemalsst.afw.table.Schema, optional

Initial schema for icSrc catalog.

**kwargs

Additional keyword arguments.

Notes

Debugging: CharacterizeImageTask has a debug dictionary with the following keys:

frame

int: if specified, the frame of first debug image displayed (defaults to 1)

repair_iter

bool; if True display image after each repair in the measure PSF loop

background_iter

bool; if True display image after each background subtraction in the measure PSF loop

measure_iter

bool; if True display image and sources at the end of each iteration of the measure PSF loop See displayAstrometry for the meaning of the various symbols.

psf

bool; if True display image and sources after PSF is measured; this will be identical to the final image displayed by measure_iter if measure_iter is true

repair

bool; if True display image and sources after final repair

measure

bool; if True display image and sources after final measurement

Attributes Summary

canMultiprocess

Methods Summary

detectMeasureAndEstimatePsf(exposure, ...)

Perform one iteration of detect, measure, and estimate PSF.

display(itemName, exposure[, sourceCat])

Display exposure and sources on next frame (for debugging).

emptyMetadata()

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

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.

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(exposure[, background, idGenerator])

Characterize a science image.

runQuantum(butlerQC, inputRefs, outputRefs)

Do butler IO and transform 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: ClassVar[bool] = True

Methods Documentation

detectMeasureAndEstimatePsf(exposure, idGenerator, background)

Perform one iteration of detect, measure, and estimate PSF.

Performs the following operations:

  • if config.doMeasurePsf or not exposure.hasPsf():

    • install a simple PSF model (replacing the existing one, if need be)

  • interpolate over cosmic rays with keepCRs=True

  • estimate background and subtract it from the exposure

  • detect, deblend and measure sources, and subtract a refined background model;

  • if config.doMeasurePsf:
    • measure PSF

Parameters:
exposurelsst.afw.image.ExposureF

Exposure to characterize.

idGeneratorlsst.meas.base.IdGenerator

Object that generates source IDs and provides RNG seeds.

backgroundlsst.afw.math.BackgroundList, optional

Initial model of background already subtracted from exposure.

Returns:
resultlsst.pipe.base.Struct

Results as a struct with attributes:

exposure

Characterized exposure (lsst.afw.image.ExposureF).

sourceCat

Detected sources (lsst.afw.table.SourceCatalog).

background

Model of subtracted background (lsst.afw.math.BackgroundList).

psfCellSet

Spatial cells of PSF candidates (lsst.afw.math.SpatialCellSet).

Raises:
LengthError

Raised if there are too many CR pixels.

display(itemName, exposure, sourceCat=None)

Display exposure and sources on next frame (for debugging).

Parameters:
itemNamestr

Name of item in debugInfo.

exposurelsst.afw.image.ExposureF

Exposure to display.

sourceCatlsst.afw.table.SourceCatalog, optional

Catalog of sources detected on the exposure.

emptyMetadata() None

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

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”.

getName() str

Get the name of the task.

Returns:
taskNamestr

Name of the task.

See also

getFullName

Get the full name of the task.

getTaskDict() dict[str, weakref.ReferenceType[lsst.pipe.base.task.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(exposure, background=None, idGenerator=None)

Characterize a science image.

Peforms the following operations: - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:

  • detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)

  • interpolate over cosmic rays

  • perform final measurement

Parameters:
exposurelsst.afw.image.ExposureF

Exposure to characterize.

backgroundlsst.afw.math.BackgroundList, optional

Initial model of background already subtracted from exposure.

idGeneratorlsst.meas.base.IdGenerator, optional

Object that generates source IDs and provides RNG seeds.

Returns:
resultlsst.pipe.base.Struct

Results as a struct with attributes:

exposure

Characterized exposure (lsst.afw.image.ExposureF).

sourceCat

Detected sources (lsst.afw.table.SourceCatalog).

background

Model of subtracted background (lsst.afw.math.BackgroundList).

psfCellSet

Spatial cells of PSF candidates (lsst.afw.math.SpatialCellSet).

characterized

Another reference to exposure for compatibility.

backgroundModel

Another reference to background for compatibility.

Raises:
RuntimeError

Raised if PSF sigma is NaN.

runQuantum(butlerQC, inputRefs, outputRefs)

Do butler IO and transform to provide in memory objects for tasks run method.

Parameters:
butlerQCQuantumContext

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.

logLevelint

A logging level constant.

See also

lsst.utils.timer.logInfo

Implementation function.

Examples

Creating a timer context:

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