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.

See also

timer.logInfo

Examples

Creating a timer context:

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