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)
`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.
`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)¶

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.

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

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

Parameters:

inputPtc : lsst.ip.isr.PtcDataset: Pre-measured PTC dataset.
inputPhotodiodeCorrection : lsst.ip.isr.PhotodiodeCorrection: Pre-measured photodiode correction used in the case when applyPhotodiodeCorrection=True.
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 geometry.
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¶