LinearitySolveTask¶

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

Bases: PipelineTask

Fit the linearity from the PTC dataset.

Attributes Summary

canMultiprocess

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

Methods Summary

`debugFit`(stepname, xVector, yVector, yModel, ...)	Debug method for linearity fitting.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`fillBadAmp`(linearizer, fitOrder, inputPtc, amp)
`fixupBadAmps`(linearizer)	Fix nan padding in bad amplifiers.
`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`(inputPtc, dummy, camera, inputDims[, ...])	Fit non-linearity to PTC data, returning the correct Linearizer object.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Ensure that the input and output dimensions are passed along.
`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

debugFit(stepname, xVector, yVector, yModel, mask, ampName)¶

Debug method for linearity fitting.

Parameters:

stepnamestr: A label to use to check if we care to debug at a given line of code.
xVectornumpy.array, (N,): The values to use as the independent variable in the linearity fit.
yVectornumpy.array, (N,): The values to use as the dependent variable in the linearity fit.
yModelnumpy.array, (N,): The values to use as the linearized result.
masknumpy.array [bool], (N,) , optional: A mask to indicate which entries of xVector and yVector to keep.
ampNamestr: Amplifier name to lookup linearity correction values.

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

fillBadAmp(linearizer, fitOrder, inputPtc, amp)¶

fixupBadAmps(linearizer)¶

Fix nan padding in bad amplifiers.

Parameters:

linearizerlsst.ip.isr.Linearizer

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(inputPtc, dummy, camera, inputDims, inputPhotodiodeCorrection=None, inputNormalization=None)¶

Fit non-linearity to PTC data, returning the correct Linearizer object.

Parameters:

inputPtclsst.ip.isr.PtcDataset: Pre-measured PTC dataset.
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 geometry.
inputDimslsst.daf.butler.DataCoordinate or dict: DataIds to use to populate the output calibration.
inputPhotodiodeCorrection: lsst.ip.isr.PhotodiodeCorrection, optional Pre-measured photodiode correction used in the case when applyPhotodiodeCorrection=True.
inputNormalizationastropy.table.Table, optional: Focal plane normalization table to use if useFocalPlaneNormalization is True.

Returns:

resultslsst.pipe.base.Struct

The results struct containing:

outputLinearizer: Final linearizer calibration (lsst.ip.isr.Linearizer).
outputProvenance: Provenance data for the new calibration (lsst.ip.isr.IsrProvenance).

Notes

This task currently fits only polynomial-defined corrections, where the correction coefficients are defined such that: \(corrImage = uncorrImage + \sum_i c_i uncorrImage^(2 + i)\) These \(c_i\) are defined in terms of the direct polynomial fit: \(meanVector ~ P(x=timeVector) = \sum_j k_j x^j\) such that \(c_(j-2) = -k_j/(k_1^j)\) in units of DN^(1-j) (c.f., Eq. 37 of 2003.05978). The config.polynomialOrder or config.splineKnots define the maximum order of \(x^j\) to fit. As \(k_0\) and \(k_1\) are degenerate with bias level and gain, they are not included in the non-linearity correction.

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.

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

LinearitySolveTask¶