BrighterFatterKernelSolveTask¶
- class lsst.cp.pipe.BrighterFatterKernelSolveTask(*, config: PipelineTaskConfig | None = None, log: logging.Logger | LsstLogAdapter | None = None, initInputs: dict[str, Any] | None = None, **kwargs: Any)¶
Bases:
PipelineTask
Measure appropriate Brighter-Fatter Kernel from the PTC dataset.
Attributes Summary
Methods Summary
averageCorrelations
(xCorrList, name)Average input correlations.
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.
getName
()Get the name of the task.
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.quadraticCorrelations
(xCorrList, fluxList, name)Measure a quadratic correlation model.
run
(inputPtc, dummy, camera, inputDims)Combine covariance information from PTC into brighter-fatter kernels.
runQuantum
(butlerQC, inputRefs, outputRefs)Ensure that the input and output dimensions are passed along.
sampleCovModel
(fluxes, noiseMatrix, gain, ...)Sample the correlation model and measure widetile{C}_{ij} from Broughton et al. 2023 (eq.
successiveOverRelax
(source[, maxIter, eLevel])An implementation of the successive over relaxation (SOR) method.
timer
(name[, logLevel])Context manager to log performance data for an arbitrary block of code.
Attributes Documentation
Methods Documentation
- averageCorrelations(xCorrList, name)¶
Average input correlations.
- Parameters:
- xCorrList
list
[numpy.array
] List of cross-correlations. These are expected to be square arrays.
- name
str
Name for log messages.
- xCorrList
- Returns:
- meanXcorr
numpy.array
, (N, N) The averaged cross-correlation.
- meanXcorr
- 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
- 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.
- 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
- 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
.
- quadraticCorrelations(xCorrList, fluxList, name)¶
Measure a quadratic correlation model.
- Parameters:
- xCorrList
list
[numpy.array
] List of cross-correlations. These are expected to be square arrays.
- fluxList
numpy.array
, (Nflux,) Associated list of fluxes.
- name
str
Name for log messages.
- xCorrList
- Returns:
- meanXcorr
numpy.array
, (N, N) The averaged cross-correlation.
- meanXcorr
- run(inputPtc, dummy, camera, inputDims)¶
Combine covariance information from PTC into brighter-fatter kernels.
- Parameters:
- inputPtc
lsst.ip.isr.PhotonTransferCurveDataset
PTC data containing per-amplifier covariance measurements.
- dummy
lsst.afw.image.Exposure
The exposure used to select the appropriate PTC dataset. In almost all circumstances, one of the input exposures used to generate the PTC dataset is the best option.
- camera
lsst.afw.cameraGeom.Camera
Camera to use for camera geometry information.
- inputDims
lsst.daf.butler.DataCoordinate
ordict
DataIds to use to populate the output calibration.
- inputPtc
- Returns:
- results
lsst.pipe.base.Struct
The resulst struct containing:
outputBfk
Resulting Brighter-Fatter Kernel (
lsst.ip.isr.BrighterFatterKernel
).
- results
- runQuantum(butlerQC, inputRefs, outputRefs)¶
Ensure that the input and output dimensions are passed along.
- Parameters:
- butlerQC
lsst.daf.butler.QuantumContext
Butler to operate on.
- inputRefs
lsst.pipe.base.InputQuantizedConnection
Input data refs to load.
- ouptutRefs
lsst.pipe.base.OutputQuantizedConnection
Output data refs to persist.
- butlerQC
- sampleCovModel(fluxes, noiseMatrix, gain, covModelList, flux, name)¶
Sample the correlation model and measure widetile{C}_{ij} from Broughton et al. 2023 (eq. 4)
- Parameters:
- fluxes
list
[float
] List of fluxes (in ADU)
- noiseMatrix
numpy.array
, (N, N) Noise matrix
- gain
float
Amplifier gain
- covModelList
numpy.array
, (N, N) List of covariance model matrices. These are expected to be square arrays.
- flux
float
Flux in electrons at which to sample the covariance model.
- name
str
Name for log messages.
- fluxes
- Returns:
- covTilde
numpy.array
, (N, N) The calculated C-tilde from Broughton et al. 2023 (eq. 4).
- covTilde
- successiveOverRelax(source, maxIter=None, eLevel=None)¶
An implementation of the successive over relaxation (SOR) method.
A numerical method for solving a system of linear equations with faster convergence than the Gauss-Seidel method.
- Parameters:
- source
numpy.ndarray
, (N, N) The input array.
- maxIter
int
, optional Maximum number of iterations to attempt before aborting.
- eLevel
float
, optional The target error level at which we deem convergence to have occurred.
- source
- Returns:
- output
numpy.ndarray
, (N, N) The solution.
- output