BrighterFatterKernelSolveTask¶

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

Bases: lsst.pipe.base.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.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`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`()	Get the schemas generated 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.
`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 = True¶

Methods Documentation

averageCorrelations(xCorrList, name)¶

Average input correlations.

Parameters:	xCorrList : `list` [`numpy.array`] List of cross-correlations. These are expected to be square arrays. name : `str` Name for log messages.
Returns:	meanXcorr : `numpy.array`, (N, N) The averaged cross-correlation.

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

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.

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

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

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")

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.

quadraticCorrelations(xCorrList, fluxList, name)¶

Measure a quadratic correlation model.

Parameters:	xCorrList : `list` [`numpy.array`] List of cross-correlations. These are expected to be square arrays. fluxList : `numpy.array`, (Nflux,) Associated list of fluxes. name : `str` Name for log messages.
Returns:	meanXcorr : `numpy.array`, (N, N) The averaged cross-correlation.

run(inputPtc, dummy, camera, inputDims)¶

Combine covariance information from PTC into brighter-fatter kernels.

Parameters:

inputPtc : lsst.ip.isr.PhotonTransferCurveDataset: PTC data containing per-amplifier covariance measurements.
dummy : lsst.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.
camera : lsst.afw.cameraGeom.Camera: Camera to use for camera geometry information.
inputDims : lsst.daf.butler.DataCoordinate or dict: DataIds to use to populate the output calibration.

Returns:

results : lsst.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:	butlerQC : `lsst.daf.butler.butlerQuantumContext.ButlerQuantumContext` Butler to operate on. inputRefs : `lsst.pipe.base.InputQuantizedConnection` Input data refs to load. ouptutRefs : `lsst.pipe.base.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:	source : `numpy.ndarray`, (N, N) The input array. maxIter : `int`, optional Maximum number of iterations to attempt before aborting. eLevel : `float`, optional The target error level at which we deem convergence to have occurred.
Returns:	output : `numpy.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:	name : `str` Name of code being timed; data will be logged using item name: `Start` and `End`. logLevel A `logging` level constant.

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BrighterFatterKernelSolveTask¶