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 - 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.

Navigation

CharacterizeImageTask¶