PhotonTransferCurveExtractPairTask¶

class lsst.cp.pipe.PhotonTransferCurveExtractPairTask(*, config: PipelineTaskConfig | None = None, log: logging.Logger | LsstLogAdapter | None = None, initInputs: dict[str, Any] | None = None, **kwargs: Any)¶

Bases: PhotonTransferCurveExtractTaskBase

Attributes Summary

canMultiprocess

Methods Summary

`computeGaussianHistogramParameters`(im1Area, ...)	Compute KS test for a Gaussian model fit to a histogram of the difference image.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getGainFromFlatPair`(im1Area, im2Area, ...[, ...])	Estimate the gain from a single pair of flats.
`getImageAreasMasksStats`(exposure1, exposure2)	Get image areas in a region as well as masks and statistic objects.
`getName`()	Get the name of the task.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`makeCovArray`(inputTuple, maxRangeFromTuple)	Make covariances array from tuple.
`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.
`measureMeanVarCov`(im1Area, im2Area, ...)	Calculate the mean of each of two exposures and the variance and covariance of their difference.
`run`(*, inputExp, inputDims[, ...])	Measure covariances from a single flat pair.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Do butler IO and transform to provide in memory objects for tasks `run` method.
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess: ClassVar[bool] = True¶

Methods Documentation

computeGaussianHistogramParameters(im1Area, im2Area, imStatsCtrl, mu1, mu2)¶

Compute KS test for a Gaussian model fit to a histogram of the difference image.

Parameters:

im1Arealsst.afw.image.MaskedImageF: Masked image from exposure 1.
im2Arealsst.afw.image.MaskedImageF: Masked image from exposure 2.
imStatsCtrllsst.afw.math.StatisticsControl: Statistics control object.
mu1float: Clipped mean of im1Area (ADU).
mu2float: Clipped mean of im2Area (ADU).

Returns:

varFitfloat: Variance from the Gaussian fit.
chi2Doffloat: Chi-squared per degree of freedom of Gaussian fit.
kspValuefloat: The KS test p-value for the Gaussian fit.

Notes

The algorithm here was originally developed by Aaron Roodman. Tests on the full focal plane of LSSTCam during testing has shown that a KS test p-value cut of 0.01 is a good discriminant for well-behaved flat pairs (p>0.01) and poorly behaved non-Gaussian flat pairs (p<0.01).

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

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”.

getGainFromFlatPair(im1Area, im2Area, imStatsCtrl, mu1, mu2, correctionType='NONE', readNoise=None)¶

Estimate the gain from a single pair of flats.

The basic premise is 1/g = <(I1 - I2)^2/(I1 + I2)> = 1/const, where I1 and I2 correspond to flats 1 and 2, respectively. Corrections for the variable QE and the read-noise are then made following the derivation in Robert Lupton’s forthcoming book, which gets

1/g = <(I1 - I2)^2/(I1 + I2)> - 1/mu(sigma^2 - 1/2g^2).

This is a quadratic equation, whose solutions are given by:

g = mu +/- sqrt(2*sigma^2 - 2*const*mu + mu^2)/(2*const*mu*2

2*sigma^2)

where ‘mu’ is the average signal level and ‘sigma’ is the amplifier’s readnoise. The positive solution will be used. The way the correction is applied depends on the value supplied for correctionType.

correctionType is one of [‘NONE’, ‘SIMPLE’ or ‘FULL’]

‘NONE’ : uses the 1/g = <(I1 - I2)^2/(I1 + I2)> formula. ‘SIMPLE’ : uses the gain from the ‘NONE’ method for the

1/2g^2 term.

‘FULL’solves the full equation for g, discarding the: non-physical solution to the resulting quadratic.

Parameters:

im1Arealsst.afw.image.maskedImage.MaskedImageF: Masked image from exposure 1.
im2Arealsst.afw.image.maskedImage.MaskedImageF: Masked image from exposure 2.
imStatsCtrllsst.afw.math.StatisticsControl: Statistics control object.
mu1: `float`: Clipped mean of im1Area (ADU).
mu2: `float`: Clipped mean of im2Area (ADU).
correctionTypestr, optional: The correction applied, one of [‘NONE’, ‘SIMPLE’, ‘FULL’]
readNoisefloat, optional: Amplifier readout noise (ADU).

Returns:

gainfloat: Gain, in e/ADU.

Raises:

RuntimeError: Raise if correctionType is not one of ‘NONE’, ‘SIMPLE’, or ‘FULL’.

getImageAreasMasksStats(exposure1, exposure2, region=None)¶

Get image areas in a region as well as masks and statistic objects.

Parameters:

exposure1lsst.afw.image.ExposureF: First exposure of flat field pair.
exposure2lsst.afw.image.ExposureF: Second exposure of flat field pair.
regionlsst.geom.Box2I, optional: Region of each exposure where to perform the calculations (e.g, an amplifier).

Returns:

im1Arealsst.afw.image.MaskedImageF: Masked image from exposure 1.
im2Arealsst.afw.image.MaskedImageF: Masked image from exposure 2.
imStatsCtrllsst.afw.math.StatisticsControl: Statistics control object.
mu1float: Clipped mean of im1Area (ADU).
mu2float: Clipped mean of im2Area (ADU).

getName() → str¶

Get the name of the task.

Returns:

taskNamestr: Name of the task.

See also

getFullName: Get the full name of the task.

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.

makeCovArray(inputTuple, maxRangeFromTuple)¶

Make covariances array from tuple.

Parameters:

inputTuplenumpy.ndarray

Structured array with rows with at least (mu, afwVar, cov, var, i, j, npix), where: mu : float

0.5*(m1 + m2), where mu1 is the mean value of flat1 and mu2 is the mean value of flat2.

afwVarfloat: Variance of difference flat, calculated with afw.
covfloat: Covariance value at lag(i, j)
varfloat: Variance(covariance value at lag(0, 0))
iint: Lag in dimension “x”.
jint: Lag in dimension “y”.
npixint: Number of pixels used for covariance calculation.

maxRangeFromTupleint

Maximum range to select from tuple.

Returns:

covnumpy.array: Covariance arrays, indexed by mean signal mu.
vCovnumpy.array: Variance of the [co]variance arrays, indexed by mean signal mu.
muValsnumpy.array: List of mean signal values.

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.

measureMeanVarCov(im1Area, im2Area, imStatsCtrl, mu1, mu2)¶

Calculate the mean of each of two exposures and the variance and covariance of their difference. The variance is calculated via afwMath, and the covariance via the methods in Astier+19 (appendix A). In theory, var = covariance[0,0]. This should be validated, and in the future, we may decide to just keep one (covariance).

Parameters:

im1Arealsst.afw.image.maskedImage.MaskedImageF: Masked image from exposure 1.
im2Arealsst.afw.image.maskedImage.MaskedImageF: Masked image from exposure 2.
imStatsCtrllsst.afw.math.StatisticsControl: Statistics control object.
mu1: `float`: Clipped mean of im1Area (ADU).
mu2: `float`: Clipped mean of im2Area (ADU).

Returns:

mufloat

0.5*(mu1 + mu2), where mu1, and mu2 are the clipped means of the regions in both exposures. If either mu1 or m2 are NaN’s, the returned value is NaN.

varDifffloat

Half of the clipped variance of the difference of the regions inthe two input exposures. If either mu1 or m2 are NaN’s, the returned value is NaN.

covDiffAstierlist or NaN

List with tuples of the form (dx, dy, var, cov, npix), where:

dxint: Lag in x
dyint: Lag in y
varfloat: Variance at (dx, dy).
covfloat: Covariance at (dx, dy).
nPixint: Number of pixel pairs used to evaluate var and cov.

rowMeanVariancefloat

Variance of the means of each row in the difference image. Taken from github.com/lsst-camera-dh/eo_pipe.

If either mu1 or m2 are NaN’s, the returned value is NaN.

run(*, inputExp, inputDims, inputPhotodiodeData=None)¶

Measure covariances from a single flat pair.

Parameters:

inputExplist [lsst.pipe.base.connections.DeferredDatasetRef]: List of 2 references to the input flat exposures.
inputDimslist [int]: List of 2 exposure numbers for the input flat exposures.
inputPhotodiodeDatalist: [lsst.pipe.base.connections.DeferredDatasetRef], optional

List of 2 references to input photodiode data.

Returns:

resultslsst.pipe.base.Struct

The resulting Struct contains:

outputCovariance: The single-pair PTC measurement lsst.ip.isr.PhotonTransferCurveDataset

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.

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.
logLevelint: A logging level constant.

Navigation

PhotonTransferCurveExtractPairTask¶