PhotonTransferCurveExtractTask¶
- class lsst.cp.pipe.PhotonTransferCurveExtractTask(*, config: PipelineTaskConfig | None = None, log: logging.Logger | LsstLogAdapter | None = None, initInputs: dict[str, Any] | None = None, **kwargs: Any)¶
Bases:
PipelineTask
Task to measure covariances from flat fields.
This task receives as input a list of flat-field images (flats), and sorts these flats in pairs taken at the same time (the task will raise if there is one one flat at a given exposure time, and it will discard extra flats if there are more than two per exposure time). This task measures the mean, variance, and covariances from a region (e.g., an amplifier) of the difference image of the two flats with the same exposure time (alternatively, all input images could have the same exposure time but their flux changed).
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). At this moment, if the two values differ by more than the value of
thresholdDiffAfwVarVsCov00
(default: 1%), a warning will be issued.The measured covariances at a given exposure time (along with other quantities such as the mean) are stored in a PTC dataset object (
PhotonTransferCurveDataset
), which gets partially filled at this stage (the remainder of the attributes of the dataset will be filled after running the second task of the PTC-measurement pipeline,PhotonTransferCurveSolveTask
).The number of partially-filled
PhotonTransferCurveDataset
objects will be less than the number of input exposures because the task combines input flats in pairs. However, it is required at this moment that the number of input dimensions matches bijectively the number of output dimensions. Therefore, a number of “dummy” PTC datasets are inserted in the output list. This output list will then be used as input of the next task in the PTC-measurement pipeline,PhotonTransferCurveSolveTask
, which will assemble the multiplePhotonTransferCurveDataset
objects into a single one in order to fit the measured covariances as a function of flux to one of three models (seePhotonTransferCurveSolveTask
for details).Reference: Astier+19: “The Shape of the Photon Transfer Curve of CCD sensors”, arXiv:1905.08677.
Attributes Summary
Methods Summary
computeGaussianHistogramParameters
(im1Area, ...)Compute KS test for a Gaussian model fit to a histogram of the difference image.
Empty (clear) the metadata for this Task and all sub-Tasks.
Get metadata for all tasks.
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.
getReadNoise
(exposureMetadata, taskMetadata, ...)Gets readout noise for an amp from ISR metadata.
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, taskMetadata[, ...])Measure covariances from difference of flat pairs
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.
Attributes Documentation
Methods Documentation
- computeGaussianHistogramParameters(im1Area, im2Area, imStatsCtrl, mu1, mu2)¶
Compute KS test for a Gaussian model fit to a histogram of the difference image.
- Parameters:
- im1Area
lsst.afw.image.MaskedImageF
Masked image from exposure 1.
- im2Area
lsst.afw.image.MaskedImageF
Masked image from exposure 2.
- imStatsCtrl
lsst.afw.math.StatisticsControl
Statistics control object.
- mu1
float
Clipped mean of im1Area (ADU).
- mu2
float
Clipped mean of im2Area (ADU).
- im1Area
- Returns:
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).
- getFullMetadata() 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.
- metadata
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”.
- fullName
- 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:
- im1Area
lsst.afw.image.maskedImage.MaskedImageF
Masked image from exposure 1.
- im2Area
lsst.afw.image.maskedImage.MaskedImageF
Masked image from exposure 2.
- imStatsCtrl
lsst.afw.math.StatisticsControl
Statistics control object.
- mu1: `float`
Clipped mean of im1Area (ADU).
- mu2: `float`
Clipped mean of im2Area (ADU).
- correctionType
str
, optional The correction applied, one of [‘NONE’, ‘SIMPLE’, ‘FULL’]
- readNoise
float
, optional Amplifier readout noise (ADU).
- im1Area
- Returns:
- gain
float
Gain, in e/ADU.
- gain
- 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:
- exposure1
lsst.afw.image.ExposureF
First exposure of flat field pair.
- exposure2
lsst.afw.image.ExposureF
Second exposure of flat field pair.
- region
lsst.geom.Box2I
, optional Region of each exposure where to perform the calculations (e.g, an amplifier).
- exposure1
- Returns:
- im1Area
lsst.afw.image.MaskedImageF
Masked image from exposure 1.
- im2Area
lsst.afw.image.MaskedImageF
Masked image from exposure 2.
- imStatsCtrl
lsst.afw.math.StatisticsControl
Statistics control object.
- mu1
float
Clipped mean of im1Area (ADU).
- mu2
float
Clipped mean of im2Area (ADU).
- im1Area
- getName() str ¶
Get the name of the task.
- Returns:
- taskName
str
Name of the task.
- taskName
See also
getFullName
Get the full name of the task.
- getReadNoise(exposureMetadata, taskMetadata, ampName)¶
Gets readout noise for an amp from ISR metadata.
If possible, this attempts to get the now-standard headers added to the exposure itself. If not found there, the ISR TaskMetadata is searched. If neither of these has the value, warn and set the read noise to NaN.
- Parameters:
- exposureMetadata
lsst.daf.base.PropertySet
Metadata to check for read noise first.
- taskMetadata
lsst.pipe.base.TaskMetadata
List of exposures metadata from ISR for this exposure.
- ampName
str
Amplifier name.
- exposureMetadata
- Returns:
- readNoise
float
The read noise for this set of exposure/amplifier.
- readNoise
- getTaskDict() dict[str, weakref.ReferenceType[lsst.pipe.base.task.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.
- taskDict
- makeCovArray(inputTuple, maxRangeFromTuple)¶
Make covariances array from tuple.
- Parameters:
- inputTuple
numpy.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.
- maxRangeFromTuple
int
Maximum range to select from tuple.
- inputTuple
- Returns:
- cov
numpy.array
Covariance arrays, indexed by mean signal mu.
- vCov
numpy.array
Variance of the [co]variance arrays, indexed by mean signal mu.
- muVals
numpy.array
List of mean signal values.
- cov
- classmethod makeField(doc: str) ConfigurableField ¶
Make a
lsst.pex.config.ConfigurableField
for this task.- Parameters:
- doc
str
Help text for the field.
- doc
- Returns:
- configurableField
lsst.pex.config.ConfigurableField
A
ConfigurableField
for this task.
- configurableField
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:
- 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
.
- name
Notes
The subtask must be defined by
Task.config.name
, an instance ofConfigurableField
orRegistryField
.
- 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:
- im1Area
lsst.afw.image.maskedImage.MaskedImageF
Masked image from exposure 1.
- im2Area
lsst.afw.image.maskedImage.MaskedImageF
Masked image from exposure 2.
- imStatsCtrl
lsst.afw.math.StatisticsControl
Statistics control object.
- mu1: `float`
Clipped mean of im1Area (ADU).
- mu2: `float`
Clipped mean of im2Area (ADU).
- im1Area
- Returns:
- mu
float
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.
- varDiff
float
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.
- covDiffAstier
list
orNaN
- rowMeanVariance
float
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.
- mu
- run(inputExp, inputDims, taskMetadata, inputPhotodiodeData=None)¶
Measure covariances from difference of flat pairs
- Parameters:
- inputExp
dict
[float
,list
Dictionary that groups references to flat-field exposures that have the same exposure time (seconds), or that groups them sequentially by their exposure id.
- inputDims
list
List of exposure IDs.
- taskMetadata
list
[lsst.pipe.base.TaskMetadata
] List of exposures metadata from ISR.
- inputPhotodiodeData
dict
[str
,lsst.ip.isr.PhotodiodeCalib
] Photodiode readings data (optional).
- inputExp
- Returns:
- results
lsst.pipe.base.Struct
The resulting Struct contains:
outputCovariances
A list containing the per-pair PTC measurements (
list
[lsst.ip.isr.PhotonTransferCurveDataset
])
- results
- runQuantum(butlerQC, inputRefs, outputRefs)¶
Ensure that the input and output dimensions are passed along.
- Parameters:
- butlerQC
QuantumContext
Butler to operate on.
- inputRefs
InputQuantizedConnection
Input data refs to load.
- ouptutRefs
OutputQuantizedConnection
Output data refs to persist.
- butlerQC