LinearitySolveTask¶

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

Bases: lsst.pipe.base.PipelineTask

Fit the linearity from the PTC dataset.

Attributes Summary

canMultiprocess

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)
`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.
`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 = True¶

Methods Documentation

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

Debug method for linearity fitting.

Parameters:

stepname : str: A label to use to check if we care to debug at a given line of code.
xVector : numpy.array, (N,): The values to use as the independent variable in the linearity fit.
yVector : numpy.array, (N,): The values to use as the dependent variable in the linearity fit.
yModel : numpy.array, (N,): The values to use as the linearized result.
mask : numpy.array [bool], (N,) , optional: A mask to indicate which entries of xVector and yVector to keep.
ampName : str: 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)¶

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

getFullName

getResourceConfig() → Optional[ResourceConfig]¶

Return resource configuration for this task.

Returns:	Object of type `ResourceConfig` or `None` if resource configuration is not defined for this task.

getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]¶

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.

run(inputPtc, dummy, camera, inputDims, inputPhotodiodeData=None, inputPhotodiodeCorrection=None)¶

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

Parameters:

inputPtc : lsst.ip.isr.PtcDataset: Pre-measured PTC dataset.
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.
inputPhotodiodeCorrection : lsst.ip.isr.PhotodiodeCorrection: Pre-measured photodiode correction used in the case when applyPhotodiodeCorrection=True.
camera : lsst.afw.cameraGeom.Camera: Camera geometry.
inputPhotodiodeData : dict [str, lsst.ip.isr.PhotodiodeCalib]: Photodiode readings data.
inputDims : lsst.daf.butler.DataCoordinate or dict: DataIds to use to populate the output calibration.

Returns:

results : lsst.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:	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.

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.

Navigation

LinearitySolveTask¶