CfhtIsrTask¶

class lsst.obs.cfht.cfhtIsrTask.CfhtIsrTask(**kwargs)¶

Bases: lsst.ip.isr.isrTask.IsrTask

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.
`convertIntToFloat`(exposure)	Convert exposure image from uint16 to float.
`darkCorrection`(exposure, darkExposure[, invert])	Apply dark correction in place.
`debugView`(exposure, stepname)	Utility function to examine ISR exposure at different stages.
`doLinearize`(detector)	Check if linearization is needed for the detector cameraGeom.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`ensureExposure`(inputExp[, camera, detectorNum])	Ensure that the data returned by Butler is a fully constructed exp.
`flatContext`(exp, flat[, dark])	Context manager that applies and removes flats and darks, if the task is configured to apply them.
`flatCorrection`(exposure, flatExposure[, invert])	Apply flat correction in place.
`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.
`getIsrExposure`(dataRef, datasetType[, …])	Retrieve a calibration dataset for removing instrument signature.
`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.
`maskAmplifier`(ccdExposure, amp, defects)	Identify bad amplifiers, saturated and suspect pixels.
`maskAndInterpolateDefects`(exposure, …)	Mask and interpolate defects using mask plane “BAD”, in place.
`maskAndInterpolateNan`(exposure)	“Mask and interpolate NaN/infs using mask plane “UNMASKEDNAN”, in place.
`maskDefect`(exposure, defectBaseList)	Mask defects using mask plane “BAD”, in place.
`maskEdges`(exposure[, numEdgePixels, …])	Mask edge pixels with applicable mask plane.
`maskNan`(exposure)	Mask NaNs using mask plane “UNMASKEDNAN”, in place.
`maskNegativeVariance`(exposure)	Identify and mask pixels with negative variance values.
`measureBackground`(exposure[, IsrQaConfig])	Measure the image background in subgrids, for quality control.
`overscanCorrection`(ccdExposure, amp)	Apply overscan correction in place.
`parseAndRun`([args, config, log, doReturnResults])	Parse an argument list and run the command.
`readIsrData`(dataRef, rawExposure)	Retrieve necessary frames for instrument signature removal.
`roughZeroPoint`(exposure)	Set an approximate magnitude zero point for the exposure.
`run`(ccdExposure[, bias, linearizer, dark, …])	Perform instrument signature removal on an exposure
`runDataRef`(sensorRef)	Perform instrument signature removal on a ButlerDataRef of a Sensor.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
`saturationDetection`(exposure, amp)	Detect and mask saturated pixels in config.saturatedMaskName.
`saturationInterpolation`(exposure)	Interpolate over saturated pixels, in place.
`suspectDetection`(exposure, amp)	Detect and mask suspect pixels in config.suspectMaskName.
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.
`updateVariance`(ampExposure, amp[, …])	Set the variance plane using the gain and read noise
`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 = 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:	config : instance 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.

convertIntToFloat(exposure)¶

Convert exposure image from uint16 to float.

If the exposure does not need to be converted, the input is immediately returned. For exposures that are converted to use floating point pixels, the variance is set to unity and the mask to zero.

Parameters:	exposure : `lsst.afw.image.Exposure` The raw exposure to be converted.
Returns:	newexposure : `lsst.afw.image.Exposure` The input `exposure`, converted to floating point pixels.
Raises:	RuntimeError Raised if the exposure type cannot be converted to float.

darkCorrection(exposure, darkExposure, invert=False)¶

Apply dark correction in place.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process. darkExposure : `lsst.afw.image.Exposure` Dark exposure of the same size as `exposure`. invert : `Bool`, optional If True, re-add the dark to an already corrected image.
Raises:	RuntimeError Raised if either `exposure` or `darkExposure` do not have their dark time defined.

See also

lsst.ip.isr.isrFunctions.darkCorrection

debugView(exposure, stepname)¶

Utility function to examine ISR exposure at different stages.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to view. stepname : `str` State of processing to view.

doLinearize(detector)¶

Check if linearization is needed for the detector cameraGeom.

Checks config.doLinearize and the linearity type of the first amplifier.

Parameters:	detector : `lsst.afw.cameraGeom.Detector` Detector to get linearity type from.
Returns:	doLinearize : `Bool` If True, linearization should be performed.

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

ensureExposure(inputExp, camera=None, detectorNum=None)¶

Ensure that the data returned by Butler is a fully constructed exp.

ISR requires exposure-level image data for historical reasons, so if we did not recieve that from Butler, construct it from what we have, modifying the input in place.

Parameters:	inputExp : `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or `lsst.afw.image.ImageF` The input data structure obtained from Butler. camera : `lsst.afw.cameraGeom.camera`, optional The camera associated with the image. Used to find the appropriate detector if detector is not already set. detectorNum : `int`, optional The detector in the camera to attach, if the detector is not already set.
Returns:	inputExp : `lsst.afw.image.Exposure` The re-constructed exposure, with appropriate detector parameters.
Raises:	TypeError Raised if the input data cannot be used to construct an exposure.

flatContext(exp, flat, dark=None)¶

Context manager that applies and removes flats and darks, if the task is configured to apply them.

Parameters:	exp : `lsst.afw.image.Exposure` Exposure to process. flat : `lsst.afw.image.Exposure` Flat exposure the same size as `exp`. dark : `lsst.afw.image.Exposure`, optional Dark exposure the same size as `exp`.
Yields:	exp : `lsst.afw.image.Exposure` The flat and dark corrected exposure.

flatCorrection(exposure, flatExposure, invert=False)¶

Apply flat correction in place.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process. flatExposure : `lsst.afw.image.Exposure` Flat exposure of the same size as `exposure`. invert : `Bool`, optional If True, unflatten an already flattened image.

See also

lsst.ip.isr.isrFunctions.flatCorrection

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”.

getIsrExposure(dataRef, datasetType, dateObs=None, immediate=True)¶

Retrieve a calibration dataset for removing instrument signature.

Parameters:	dataRef : `daf.persistence.butlerSubset.ButlerDataRef` DataRef of the detector data to find calibration datasets for. datasetType : `str` Type of dataset to retrieve (e.g. ‘bias’, ‘flat’, etc). dateObs : `str`, optional Date of the observation. Used to correct butler failures when using fallback filters. immediate : `Bool` If True, disable butler proxies to enable error handling within this routine.
Returns:	exposure : `lsst.afw.image.Exposure` Requested calibration frame.
Raises:	RuntimeError Raised if no matching calibration frame can be found.

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 `~config.ResourceConfig` or ``None`` if resource configuration is not defined for this task.

getSchemaCatalogs() → Dict[str, Any]¶

Get the schemas generated by this task.

Returns:	schemaCatalogs : `dict` 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, weakref.ReferenceType[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.

maskAmplifier(ccdExposure, amp, defects)¶

Identify bad amplifiers, saturated and suspect pixels.

Parameters:	ccdExposure : `lsst.afw.image.Exposure` Input exposure to be masked. amp : `lsst.afw.table.AmpInfoCatalog` Catalog of parameters defining the amplifier on this exposure to mask. defects : `lsst.ip.isr.Defects` List of defects. Used to determine if the entire amplifier is bad.
Returns:	badAmp : `Bool` If this is true, the entire amplifier area is covered by defects and unusable.

maskAndInterpolateDefects(exposure, defectBaseList)¶

Mask and interpolate defects using mask plane “BAD”, in place.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process. defectBaseList : `lsst.ip.isr.Defects` or `list` of `lsst.afw.image.DefectBase`. List of defects to mask and interpolate.

See also

lsst.ip.isr.isrTask.maskDefect

maskAndInterpolateNan(exposure)¶

“Mask and interpolate NaN/infs using mask plane “UNMASKEDNAN”, in place.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process.

See also

lsst.ip.isr.isrTask.maskNan

maskDefect(exposure, defectBaseList)¶

Mask defects using mask plane “BAD”, in place.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process. defectBaseList : `lsst.ip.isr.Defects` or `list` of `lsst.afw.image.DefectBase`. List of defects to mask.

Notes

Call this after CCD assembly, since defects may cross amplifier boundaries.

maskEdges(exposure, numEdgePixels=0, maskPlane='SUSPECT', level='DETECTOR')¶

Mask edge pixels with applicable mask plane.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process. numEdgePixels : `int`, optional Number of edge pixels to mask. maskPlane : `str`, optional Mask plane name to use. level : `str`, optional Level at which to mask edges.

maskNan(exposure)¶

Mask NaNs using mask plane “UNMASKEDNAN”, in place.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process.

Notes

We mask over all non-finite values (NaN, inf), including those that are masked with other bits (because those may or may not be interpolated over later, and we want to remove all NaN/infs). Despite this behaviour, the “UNMASKEDNAN” mask plane is used to preserve the historical name.

maskNegativeVariance(exposure)¶

Identify and mask pixels with negative variance values.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process.

See also

lsst.ip.isr.isrFunctions.updateVariance

measureBackground(exposure, IsrQaConfig=None)¶

Measure the image background in subgrids, for quality control.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process. IsrQaConfig : `lsst.ip.isr.isrQa.IsrQaConfig` Configuration object containing parameters on which background statistics and subgrids to use.

overscanCorrection(ccdExposure, amp)¶

Apply overscan correction in place.

This method does initial pixel rejection of the overscan region. The overscan can also be optionally segmented to allow for discontinuous overscan responses to be fit separately. The actual overscan subtraction is performed by the lsst.ip.isr.isrFunctions.overscanCorrection function, which is called here after the amplifier is preprocessed.

Parameters:	ccdExposure : `lsst.afw.image.Exposure` Exposure to have overscan correction performed. amp : `lsst.afw.cameraGeom.Amplifer` The amplifier to consider while correcting the overscan.
Returns:	overscanResults : `lsst.pipe.base.Struct` Result struct with components: - `imageFit` : scalar or `lsst.afw.image.Image` Value or fit subtracted from the amplifier image data. `overscanFit` : scalar or `lsst.afw.image.Image` Value or fit subtracted from the overscan image data. `overscanImage` : `lsst.afw.image.Image` Image of the overscan region with the overscan correction applied. This quantity is used to estimate the amplifier read noise empirically.
Raises:	RuntimeError Raised if the `amp` does not contain raw pixel information.

See also

lsst.ip.isr.isrFunctions.overscanCorrection

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

Parse an argument list and run the command.

Parameters:

args : list, optional: List of command-line arguments; if None use sys.argv.
config : lsst.pex.config.Config-type, optional: Config for task. If None use Task.ConfigClass.
log : logging.Logger-type, optional: Log. If None use the default log.
doReturnResults : bool, 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:

struct : lsst.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.

readIsrData(dataRef, rawExposure)¶

Retrieve necessary frames for instrument signature removal.

Pre-fetching all required ISR data products limits the IO required by the ISR. Any conflict between the calibration data available and that needed for ISR is also detected prior to doing processing, allowing it to fail quickly.

Parameters:	dataRef : `daf.persistence.butlerSubset.ButlerDataRef` Butler reference of the detector data to be processed rawExposure : `afw.image.Exposure` The raw exposure that will later be corrected with the retrieved calibration data; should not be modified in this method.
Returns:	result : `lsst.pipe.base.Struct` Result struct with components (which may be `None`): - `bias`: bias calibration frame (`afw.image.Exposure`) - `linearizer`: functor for linearization (`ip.isr.linearize.LinearizeBase`) `crosstalkSources`: list of possible crosstalk sources (`list`) `dark`: dark calibration frame (`afw.image.Exposure`) `flat`: flat calibration frame (`afw.image.Exposure`) `bfKernel`: Brighter-Fatter kernel (`numpy.ndarray`) `defects`: list of defects (`lsst.ip.isr.Defects`) `fringes`: `lsst.pipe.base.Struct` with components: - `fringes`: fringe calibration frame (`afw.image.Exposure`) - `seed`: random seed derived from the ccdExposureId for random number generator (`uint32`). `opticsTransmission`: `lsst.afw.image.TransmissionCurve` A `TransmissionCurve` that represents the throughput of the optics, to be evaluated in focal-plane coordinates. `filterTransmission` : `lsst.afw.image.TransmissionCurve` A `TransmissionCurve` that represents the throughput of the filter itself, to be evaluated in focal-plane coordinates. `sensorTransmission` : `lsst.afw.image.TransmissionCurve` A `TransmissionCurve` that represents the throughput of the sensor itself, to be evaluated in post-assembly trimmed detector coordinates. `atmosphereTransmission` : `lsst.afw.image.TransmissionCurve` A `TransmissionCurve` that represents the throughput of the atmosphere, assumed to be spatially constant. `strayLightData` : `object` An opaque object containing calibration information for stray-light correction. If `None`, no correction will be performed. `illumMaskedImage` : illumination correction image (`lsst.afw.image.MaskedImage`)
Raises:	NotImplementedError : Raised if a per-amplifier brighter-fatter kernel is requested by the configuration.

roughZeroPoint(exposure)¶

Set an approximate magnitude zero point for the exposure.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process.

run(ccdExposure, bias=None, linearizer=None, dark=None, flat=None, defects=None, fringes=None, bfKernel=None, **kwds)¶

Perform instrument signature removal on an exposure

Steps include: - Detect saturation, apply overscan correction, bias, dark and flat - Perform CCD assembly - Interpolate over defects, saturated pixels and all NaNs - Persist the ISR-corrected exposure as “postISRCCD” if

config.doWrite is True

Parameters:

ccdExposure : lsst.afw.image.Exposure: Detector data.
bias : lsst.afw.image.exposure: Exposure of bias frame.
linearizer : lsst.ip.isr.LinearizeBase callable: Linearizing functor; a subclass of lsst.ip.isr.LinearizeBase.
dark : lsst.afw.image.exposure: Exposure of dark frame.
flat : lsst.afw.image.exposure: Exposure of flatfield.
defects : list: list of detects
fringes : lsst.afw.image.Exposure or list lsst.afw.image.Exposure: exposure of fringe frame or list of fringe exposure
bfKernel : None: kernel used for brighter-fatter correction; currently unsupported
**kwds : dict: additional kwargs forwarded to IsrTask.run.

Returns:

struct : lsst.pipe.base.Struct with fields:

exposure: the exposure after application of ISR

runDataRef(sensorRef)¶

Perform instrument signature removal on a ButlerDataRef of a Sensor.

This method contains the CmdLineTask interface to the ISR processing. All IO is handled here, freeing the run() method to manage only pixel-level calculations. The steps performed are: - Read in necessary detrending/isr/calibration data. - Process raw exposure in run(). - Persist the ISR-corrected exposure as “postISRCCD” if

config.doWrite=True.

Parameters:	sensorRef : `daf.persistence.butlerSubset.ButlerDataRef` DataRef of the detector data to be processed
Returns:	result : `lsst.pipe.base.Struct` Result struct with component: - `exposure` : `afw.image.Exposure` The fully ISR corrected exposure.
Raises:	RuntimeError Raised if a configuration option is set to True, but the required calibration data does not exist.

runQuantum(butlerQC, inputRefs, outputRefs)¶

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQC : ButlerQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefs : InputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.
outputRefs : OutputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

saturationDetection(exposure, amp)¶

Detect and mask saturated pixels in config.saturatedMaskName.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process. Only the amplifier DataSec is processed. amp : `lsst.afw.table.AmpInfoCatalog` Amplifier detector data.

See also

lsst.ip.isr.isrFunctions.makeThresholdMask

saturationInterpolation(exposure)¶

Interpolate over saturated pixels, in place.

This method should be called after saturationDetection, to ensure that the saturated pixels have been identified in the SAT mask. It should also be called after assembleCcd, since saturated regions may cross amplifier boundaries.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process.

See also

lsst.ip.isr.isrTask.saturationDetection, lsst.ip.isr.isrFunctions.interpolateFromMask

suspectDetection(exposure, amp)¶

Detect and mask suspect pixels in config.suspectMaskName.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to process. Only the amplifier DataSec is processed. amp : `lsst.afw.table.AmpInfoCatalog` Amplifier detector data.

See also

lsst.ip.isr.isrFunctions.makeThresholdMask

Notes

Suspect pixels are pixels whose value is greater than amp.getSuspectLevel(). This is intended to indicate pixels that may be affected by unknown systematics; for example if non-linearity corrections above a certain level are unstable then that would be a useful value for suspectLevel. A value of nan indicates that no such level exists and no pixels are to be masked as suspicious.

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

updateVariance(ampExposure, amp, overscanImage=None, ptcDataset=None)¶

Set the variance plane using the gain and read noise

The read noise is calculated from the overscanImage if the doEmpiricalReadNoise option is set in the configuration; otherwise the value from the amplifier data is used.

Parameters:	ampExposure : `lsst.afw.image.Exposure` Exposure to process. amp : `lsst.afw.table.AmpInfoRecord` or `FakeAmp` Amplifier detector data. overscanImage : `lsst.afw.image.MaskedImage`, optional. Image of overscan, required only for empirical read noise. ptcDataset : `lsst.ip.isr.PhotonTransferCurveDataset`, optional PTC dataset containing the gains and read noise.
Raises:	RuntimeError Raised if either `usePtcGains` of `usePtcReadNoise` are `True`, but ptcDataset is not provided. Raised if `doEmpiricalReadNoise is `True` but `overscanImage` is `None`.

See also

lsst.ip.isr.isrFunctions.updateVariance

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:

butler : lsst.daf.persistence.Butler

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

clobber : bool, 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.

doBackup : bool, 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:

butler : lsst.daf.persistence.Butler

Data butler used to read/write the package versions.

clobber : bool, 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.

doBackup : bool, optional

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

dataset : str, 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:

butler : lsst.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.

clobber : bool, 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.

doBackup : bool, 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.

Navigation

CfhtIsrTask¶