IsrTask¶

class lsst.ip.isr.IsrTask(**kwargs)¶

Bases: lsst.pipe.base.PipelineTask

Apply common instrument signature correction algorithms to a raw frame.

The process for correcting imaging data is very similar from camera to camera. This task provides a vanilla implementation of doing these corrections, including the ability to turn certain corrections off if they are not needed. The inputs to the primary method, run(), are a raw exposure to be corrected and the calibration data products. The raw input is a single chip sized mosaic of all amps including overscans and other non-science pixels.

The __init__ method sets up the subtasks for ISR processing, using the defaults from lsst.ip.isr.

Parameters:	args : `list` Positional arguments passed to the Task constructor. None used at this time. kwargs : `dict`, optional Keyword arguments passed on to the Task constructor. None used at this time.

Attributes Summary

canMultiprocess

Methods Summary

`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.
`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.
`roughZeroPoint`(exposure)	Set an approximate magnitude zero point for the exposure.
`run`(ccdExposure, *[, camera, bias, …])	Perform instrument signature removal on an exposure.
`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

Attributes Documentation

canMultiprocess = True¶

Methods Documentation

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` image-type. The input data structure obtained from Butler. Can be `lsst.afw.image.Exposure`, `lsst.afw.image.DecoratedImageU`, or `lsst.afw.image.ImageF` 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”.

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.

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]¶

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.cameraGeom.Amplifier` 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 : defects-like List of defects to mask and interpolate. Can be `lsst.ip.isr.Defects` or `list` of `lsst.afw.image.DefectBase`.

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 : defect-type List of defects to mask. Can be of type `lsst.ip.isr.Defects` or `list` of `lsst.afw.image.DefectBase`.

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.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.overscan.OverscanTask, 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` Value or fit subtracted from the amplifier image data. (scalar or `lsst.afw.image.Image`) `overscanFit` Value or fit subtracted from the overscan image data. (scalar or `lsst.afw.image.Image`) `overscanImage` Image of the overscan region with the overscan correction applied. This quantity is used to estimate the amplifier read noise empirically. (`lsst.afw.image.Image`) `edgeMask` Mask of the suspect pixels. (`lsst.afw.image.Mask`) `overscanMean` Median overscan fit value. (`float`) `overscanSigma` Clipped standard deviation of the overscan after correction. (`float`)
Raises:	RuntimeError Raised if the `amp` does not contain raw pixel information.

See also

lsst.ip.isr.overscan.OverscanTask

roughZeroPoint(exposure)¶

Set an approximate magnitude zero point for the exposure.

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

run(ccdExposure, *, camera=None, bias=None, linearizer=None, crosstalk=None, crosstalkSources=None, dark=None, flat=None, ptc=None, bfKernel=None, bfGains=None, defects=None, fringes=Struct(fringes=None), opticsTransmission=None, filterTransmission=None, sensorTransmission=None, atmosphereTransmission=None, detectorNum=None, strayLightData=None, illumMaskedImage=None, deferredCharge=None)¶

Perform instrument signature removal on an exposure.

Steps included in the ISR processing, in order performed, are:

saturation and suspect pixel masking
overscan subtraction
CCD assembly of individual amplifiers
bias subtraction
variance image construction
linearization of non-linear response
crosstalk masking
brighter-fatter correction
dark subtraction
fringe correction
stray light subtraction
flat correction
masking of known defects and camera specific features
vignette calculation
appending transmission curve and distortion model

Parameters:	ccdExposure : `lsst.afw.image.Exposure` The raw exposure that is to be run through ISR. The exposure is modified by this method. camera : `lsst.afw.cameraGeom.Camera`, optional The camera geometry for this exposure. Required if one or more of `ccdExposure`, `bias`, `dark`, or `flat` does not have an associated detector. bias : `lsst.afw.image.Exposure`, optional Bias calibration frame. linearizer : `lsst.ip.isr.LinearizeBase`, optional Functor for linearization. crosstalk : `lsst.ip.isr.CrosstalkCalib`, optional Calibration for crosstalk. crosstalkSources : `list`, optional List of possible crosstalk sources. dark : `lsst.afw.image.Exposure`, optional Dark calibration frame. flat : `lsst.afw.image.Exposure`, optional Flat calibration frame. ptc : `lsst.ip.isr.PhotonTransferCurveDataset`, optional Photon transfer curve dataset, with, e.g., gains and read noise. bfKernel : `numpy.ndarray`, optional Brighter-fatter kernel. bfGains : `dict` of `float`, optional Gains used to override the detector’s nominal gains for the brighter-fatter correction. A dict keyed by amplifier name for the detector in question. defects : `lsst.ip.isr.Defects`, optional List of defects. fringes : `lsst.pipe.base.Struct`, optional Struct containing the fringe correction data, with elements: `fringes` fringe calibration frame (`lsst.afw.image.Exposure`) `seed` random seed derived from the `ccdExposureId` for random number generator (`numpy.uint32`) opticsTransmission: `lsst.afw.image.TransmissionCurve`, optional 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. detectorNum : `int`, optional The integer number for the detector to process. strayLightData : `object`, optional Opaque object containing calibration information for stray-light correction. If `None`, no correction will be performed. illumMaskedImage : `lsst.afw.image.MaskedImage`, optional Illumination correction image.
Returns:	result : `lsst.pipe.base.Struct` Result struct with component: `exposure` The fully ISR corrected exposure. (`lsst.afw.image.Exposure`) `outputExposure` An alias for `exposure`. (`lsst.afw.image.Exposure`) `ossThumb` Thumbnail image of the exposure after overscan subtraction. (`numpy.ndarray`) `flattenedThumb` Thumbnail image of the exposure after flat-field correction. (`numpy.ndarray`) `outputStatistics` Values of the additional statistics calculated.
Raises:	RuntimeError Raised if a configuration option is set to `True`, but the required calibration data has not been specified.

Notes

The current processed exposure can be viewed by setting the appropriate lsstDebug entries in the debug.display dictionary. The names of these entries correspond to some of the IsrTaskConfig Boolean options, with the value denoting the frame to use. The exposure is shown inside the matching option check and after the processing of that step has finished. The steps with debug points are:

doAssembleCcd
doBias
doCrosstalk
doBrighterFatter
doDark
doFringe
doStrayLight
doFlat

In addition, setting the postISRCCD entry displays the exposure after all ISR processing has finished.

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.cameraGeom.Amplifier` 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.cameraGeom.Amplifier` 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.cameraGeom.Amplifier` 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

Navigation

IsrTask¶