LinearitySolveTask

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

Bases: PipelineTask, CmdLineTask

Fit the linearity from the PTC dataset.

Attributes Summary

canMultiprocess

Methods Summary

applyOverrides(config)

A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.

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.

parseAndRun([args, config, log, doReturnResults])

Parse an argument list and run the command.

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.

writeConfig(butler[, clobber, doBackup])

Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.

writeMetadata(dataRef)

Write the metadata produced from processing the data.

writePackageVersions(butler[, clobber, ...])

Compare and write package versions.

writeSchemas(butler[, clobber, doBackup])

Write the schemas returned by lsst.pipe.base.Task.getAllSchemaCatalogs.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True

Methods Documentation

classmethod applyOverrides(config)

A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.

Parameters:
configinstance of task’s ConfigClass

Task configuration.

Notes

This is necessary in some cases because the camera-specific overrides may retarget subtasks, wiping out changes made in ConfigClass.setDefaults. See LSST Trac ticket #2282 for more discussion.

Warning

This is called by CmdLineTask.parseAndRun; other ways of constructing a config will not apply these overrides.

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)
getAllSchemaCatalogs() Dict[str, Any]

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:
schemacatalogsdict

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() 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
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.
getSchemaCatalogs() Dict[str, Any]

Get the schemas generated by this task.

Returns:
schemaCatalogsdict

Keys are butler dataset type, values are an empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for this 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, ReferenceType[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.

classmethod parseAndRun(args=None, config=None, log=None, doReturnResults=False)

Parse an argument list and run the command.

Parameters:
argslist, optional

List of command-line arguments; if None use sys.argv.

configlsst.pex.config.Config-type, optional

Config for task. If None use Task.ConfigClass.

loglogging.Logger-type, optional

Log. If None use the default log.

doReturnResultsbool, optional

If True, return the results of this task. Default is False. This is only intended for unit tests and similar use. It can easily exhaust memory (if the task returns enough data and you call it enough times) and it will fail when using multiprocessing if the returned data cannot be pickled.

Returns:
structlsst.pipe.base.Struct

Fields are:

argumentParser

the argument parser (lsst.pipe.base.ArgumentParser).

parsedCmd

the parsed command returned by the argument parser’s parse_args method (argparse.Namespace).

taskRunner

the task runner used to run the task (an instance of Task.RunnerClass).

resultList

results returned by the task runner’s run method, one entry per invocation (list). This will typically be a list of Struct, each containing at least an exitStatus integer (0 or 1); see Task.RunnerClass (TaskRunner by default) for more details.

Notes

Calling this method with no arguments specified is the standard way to run a command-line task from the command-line. For an example see pipe_tasks bin/makeSkyMap.py or almost any other file in that directory.

If one or more of the dataIds fails then this routine will exit (with a status giving the number of failed dataIds) rather than returning this struct; this behaviour can be overridden by specifying the --noExit command-line option.

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

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

Parameters:
inputPtclsst.ip.isr.PtcDataset

Pre-measured PTC dataset.

inputPhotodiodeCorrectionlsst.ip.isr.PhotodiodeCorrection

Pre-measured photodiode correction used in the case when applyPhotodiodeCorrection=True.

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.

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.butlerQuantumContext.ButlerQuantumContext

Butler to operate on.

inputRefslsst.pipe.base.connections.InputQuantizedConnection

Input data refs to load.

ouptutRefslsst.pipe.base.connections.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.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time
writeConfig(butler, clobber=False, doBackup=True)

Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.

Parameters:
butlerlsst.daf.persistence.Butler

Data butler used to write the config. The config is written to dataset type CmdLineTask._getConfigName.

clobberbool, optional

A boolean flag that controls what happens if a config already has been saved:

  • True: overwrite or rename the existing config, depending on doBackup.

  • False: raise TaskError if this config does not match the existing config.

doBackupbool, optional

Set to True to backup the config files if clobbering.

writeMetadata(dataRef)

Write the metadata produced from processing the data.

Parameters:
dataRef

Butler data reference used to write the metadata. The metadata is written to dataset type CmdLineTask._getMetadataName.

writePackageVersions(butler, clobber=False, doBackup=True, dataset='packages')

Compare and write package versions.

Parameters:
butlerlsst.daf.persistence.Butler

Data butler used to read/write the package versions.

clobberbool, optional

A boolean flag that controls what happens if versions already have been saved:

  • True: overwrite or rename the existing version info, depending on doBackup.

  • False: raise TaskError if this version info does not match the existing.

doBackupbool, optional

If True and clobbering, old package version files are backed up.

datasetstr, optional

Name of dataset to read/write.

Raises:
TaskError

Raised if there is a version mismatch with current and persisted lists of package versions.

Notes

Note that this operation is subject to a race condition.

writeSchemas(butler, clobber=False, doBackup=True)

Write the schemas returned by lsst.pipe.base.Task.getAllSchemaCatalogs.

Parameters:
butlerlsst.daf.persistence.Butler

Data butler used to write the schema. Each schema is written to the dataset type specified as the key in the dict returned by getAllSchemaCatalogs.

clobberbool, optional

A boolean flag that controls what happens if a schema already has been saved:

  • True: overwrite or rename the existing schema, depending on doBackup.

  • False: raise TaskError if this schema does not match the existing schema.

doBackupbool, optional

Set to True to backup the schema files if clobbering.

Notes

If clobber is False and an existing schema does not match a current schema, then some schemas may have been saved successfully and others may not, and there is no easy way to tell which is which.