IsrTask

class lsst.ip.isr.IsrTask(**kwargs)

Bases: 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:
argslist

Positional arguments passed to the Task constructor. None used at this time.

kwargsdict, optional

Keyword arguments passed on to the Task constructor. None used at this time.

Attributes Summary

canMultiprocess

Methods Summary

compareCameraKeywords(exposureMetadata, ...)

Compare header keywords to confirm camera states match.

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.

extractCalibDate(calib)

Extract common calibration metadata values that will be written to output header.

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.

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.

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)

Do butler IO and transform 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: ClassVar[bool] = True

Methods Documentation

compareCameraKeywords(exposureMetadata, calib, calibName)

Compare header keywords to confirm camera states match.

Parameters:
exposureMetadatalsst.daf.base.PropertySet

Header for the exposure being processed.

caliblsst.afw.image.Exposure or lsst.ip.isr.IsrCalib

Calibration to be applied.

calibNamestr

Calib type for log message.

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:
exposurelsst.afw.image.Exposure

The raw exposure to be converted.

Returns:
newexposurelsst.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:
exposurelsst.afw.image.Exposure

Exposure to process.

darkExposurelsst.afw.image.Exposure

Dark exposure of the same size as exposure.

invertBool, 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:
exposurelsst.afw.image.Exposure

Exposure to view.

stepnamestr

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:
detectorlsst.afw.cameraGeom.Detector

Detector to get linearity type from.

Returns:
doLinearizeBool

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:
inputExplsst.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

cameralsst.afw.cameraGeom.camera, optional

The camera associated with the image. Used to find the appropriate detector if detector is not already set.

detectorNumint, optional

The detector in the camera to attach, if the detector is not already set.

Returns:
inputExplsst.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.

static extractCalibDate(calib)

Extract common calibration metadata values that will be written to output header.

Parameters:
caliblsst.afw.image.Exposure or lsst.ip.isr.IsrCalib

Calibration to pull date information from.

Returns:
dateStringstr

Calibration creation date string to add to header.

flatContext(exp, flat, dark=None)

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

Parameters:
explsst.afw.image.Exposure

Exposure to process.

flatlsst.afw.image.Exposure

Flat exposure the same size as exp.

darklsst.afw.image.Exposure, optional

Dark exposure the same size as exp.

Yields:
explsst.afw.image.Exposure

The flat and dark corrected exposure.

flatCorrection(exposure, flatExposure, invert=False)

Apply flat correction in place.

Parameters:
exposurelsst.afw.image.Exposure

Exposure to process.

flatExposurelsst.afw.image.Exposure

Flat exposure of the same size as exposure.

invertBool, optional

If True, unflatten an already flattened image.

See also

lsst.ip.isr.isrFunctions.flatCorrection
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
getTaskDict() dict[str, weakref.ReferenceType[lsst.pipe.base.task.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.

maskAmplifier(ccdExposure, amp, defects)

Identify bad amplifiers, saturated and suspect pixels.

Parameters:
ccdExposurelsst.afw.image.Exposure

Input exposure to be masked.

amplsst.afw.cameraGeom.Amplifier

Catalog of parameters defining the amplifier on this exposure to mask.

defectslsst.ip.isr.Defects

List of defects. Used to determine if the entire amplifier is bad.

Returns:
badAmpBool

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:
exposurelsst.afw.image.Exposure

Exposure to process.

defectBaseListdefects-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:
exposurelsst.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:
exposurelsst.afw.image.Exposure

Exposure to process.

defectBaseListdefect-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:
exposurelsst.afw.image.Exposure

Exposure to process.

numEdgePixelsint, optional

Number of edge pixels to mask.

maskPlanestr, optional

Mask plane name to use.

levelstr, optional

Level at which to mask edges.

maskNan(exposure)

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

Parameters:
exposurelsst.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:
exposurelsst.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:
exposurelsst.afw.image.Exposure

Exposure to process.

IsrQaConfiglsst.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.overscan.OverscanTask, which is called here after the amplifier is preprocessed.

Parameters:
ccdExposurelsst.afw.image.Exposure

Exposure to have overscan correction performed.

amplsst.afw.cameraGeom.Amplifer

The amplifier to consider while correcting the overscan.

Returns:
overscanResultslsst.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:
exposurelsst.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, deferredChargeCalib=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:
ccdExposurelsst.afw.image.Exposure

The raw exposure that is to be run through ISR. The exposure is modified by this method.

cameralsst.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.

biaslsst.afw.image.Exposure, optional

Bias calibration frame.

linearizerlsst.ip.isr.linearize.LinearizeBase, optional

Functor for linearization.

crosstalklsst.ip.isr.crosstalk.CrosstalkCalib, optional

Calibration for crosstalk.

crosstalkSourceslist, optional

List of possible crosstalk sources.

darklsst.afw.image.Exposure, optional

Dark calibration frame.

flatlsst.afw.image.Exposure, optional

Flat calibration frame.

ptclsst.ip.isr.PhotonTransferCurveDataset, optional

Photon transfer curve dataset, with, e.g., gains and read noise.

bfKernelnumpy.ndarray, optional

Brighter-fatter kernel.

bfGainsdict 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.

defectslsst.ip.isr.Defects, optional

List of defects.

fringeslsst.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.

filterTransmissionlsst.afw.image.TransmissionCurve

A TransmissionCurve that represents the throughput of the filter itself, to be evaluated in focal-plane coordinates.

sensorTransmissionlsst.afw.image.TransmissionCurve

A TransmissionCurve that represents the throughput of the sensor itself, to be evaluated in post-assembly trimmed detector coordinates.

atmosphereTransmissionlsst.afw.image.TransmissionCurve

A TransmissionCurve that represents the throughput of the atmosphere, assumed to be spatially constant.

detectorNumint, optional

The integer number for the detector to process.

strayLightDataobject, optional

Opaque object containing calibration information for stray-light correction. If None, no correction will be performed.

illumMaskedImagelsst.afw.image.MaskedImage, optional

Illumination correction image.

Returns:
resultlsst.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)

Do butler IO and transform to provide in memory objects for tasks run method.

Parameters:
butlerQCQuantumContext

A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.

inputRefsInputQuantizedConnection

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.

outputRefsOutputQuantizedConnection

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:
exposurelsst.afw.image.Exposure

Exposure to process. Only the amplifier DataSec is processed.

amplsst.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:
exposurelsst.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:
exposurelsst.afw.image.Exposure

Exposure to process. Only the amplifier DataSec is processed.

amplsst.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:
namestr

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

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:
ampExposurelsst.afw.image.Exposure

Exposure to process.

amplsst.afw.cameraGeom.Amplifier or FakeAmp

Amplifier detector data.

overscanImagelsst.afw.image.MaskedImage, optional.

Image of overscan, required only for empirical read noise.

ptcDatasetlsst.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