BrighterFatterKernelSolveTask

class lsst.cp.pipe.BrighterFatterKernelSolveTask(*, config: PipelineTaskConfig | None = None, log: logging.Logger | LsstLogAdapter | None = None, initInputs: dict[str, Any] | None = None, **kwargs: Any)

Bases: PipelineTask

Measure appropriate Brighter-Fatter Kernel from the PTC dataset.

Attributes Summary

canMultiprocess

Methods Summary

averageCorrelations(xCorrList, name)	Average input correlations.
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.
quadraticCorrelations(xCorrList, fluxList, name)	Measure a quadratic correlation model.
run(inputPtc, dummy, camera, inputDims)	Combine covariance information from PTC into brighter-fatter kernels.
runQuantum(butlerQC, inputRefs, outputRefs)	Ensure that the input and output dimensions are passed along.
successiveOverRelax(source[, maxIter, eLevel])	An implementation of the successive over relaxation (SOR) method.
timer(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True

Methods Documentation

averageCorrelations(xCorrList, name)

Average input correlations.

Parameters:

xCorrListlist [numpy.array]

List of cross-correlations. These are expected to be square arrays.

namestr

Name for log messages.

Returns:

meanXcorrnumpy.array, (N, N)

The averaged cross-correlation.

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

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.

quadraticCorrelations(xCorrList, fluxList, name)

Measure a quadratic correlation model.

Parameters:

xCorrListlist [numpy.array]

List of cross-correlations. These are expected to be square arrays.

fluxListnumpy.array, (Nflux,)

Associated list of fluxes.

namestr

Name for log messages.

Returns:

meanXcorrnumpy.array, (N, N)

The averaged cross-correlation.

run(inputPtc, dummy, camera, inputDims)

Combine covariance information from PTC into brighter-fatter kernels.

Parameters:

inputPtclsst.ip.isr.PhotonTransferCurveDataset

PTC data containing per-amplifier covariance measurements.

dummylsst.afw.image.Exposure

The exposure used to select the appropriate PTC dataset. In almost all circumstances, one of the input exposures used to generate the PTC dataset is the best option.

cameralsst.afw.cameraGeom.Camera

Camera to use for camera geometry information.

inputDimslsst.daf.butler.DataCoordinate or dict

DataIds to use to populate the output calibration.

Returns:

resultslsst.pipe.base.Struct

The resulst struct containing:

outputBfk

Resulting Brighter-Fatter Kernel (lsst.ip.isr.BrighterFatterKernel).

runQuantum(butlerQC, inputRefs, outputRefs)

Ensure that the input and output dimensions are passed along.

Parameters:

butlerQClsst.daf.butler.butlerQuantumContext.ButlerQuantumContext

Butler to operate on.

inputRefslsst.pipe.base.connections.InputQuantizedConnection

Input data refs to load.

ouptutRefslsst.pipe.base.connections.OutputQuantizedConnection

Output data refs to persist.

successiveOverRelax(source, maxIter=None, eLevel=None)

An implementation of the successive over relaxation (SOR) method.

A numerical method for solving a system of linear equations with faster convergence than the Gauss-Seidel method.

Parameters:

sourcenumpy.ndarray, (N, N)

The input array.

maxIterint, optional

Maximum number of iterations to attempt before aborting.

eLevelfloat, optional

The target error level at which we deem convergence to have occurred.

Returns:

outputnumpy.ndarray, (N, N)

The solution.

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

lsst.utils.timer.logInfo

Examples

Creating a timer context:

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