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

Whether this task can be run by an executor that uses subprocesses for parallelism.

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.
`sampleCovModel`(fluxes, noiseMatrix, gain, ...)	Sample the correlation model and measure widetile{C}_{ij} from Broughton et al. 2023 (eq.
`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¶: Whether this task can be run by an executor that uses subprocesses for parallelism.

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: 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.

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.QuantumContext: Butler to operate on.
inputRefslsst.pipe.base.InputQuantizedConnection: Input data refs to load.
ouptutRefslsst.pipe.base.OutputQuantizedConnection: Output data refs to persist.

sampleCovModel(fluxes, noiseMatrix, gain, covModelList, flux, name)¶

Sample the correlation model and measure widetile{C}_{ij} from Broughton et al. 2023 (eq. 4)

Parameters:

fluxeslist [float]: List of fluxes (in ADU)
noiseMatrixnumpy.array, (N, N): Noise matrix
gainfloat: Amplifier gain
covModelListnumpy.array, (N, N): List of covariance model matrices. These are expected to be square arrays.
fluxfloat: Flux in electrons at which to sample the covariance model.
namestr: Name for log messages.

Returns:

covTildenumpy.array, (N, N): The calculated C-tilde from Broughton et al. 2023 (eq. 4).

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.
logLevelint: A logging level constant.

Navigation

BrighterFatterKernelSolveTask¶