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.

Methods Summary

averageCorrelations(xCorrList, name)	Average input correlations.
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.

Methods Documentation

averageCorrelations(xCorrList, name)

Average input correlations.

Parameters

xCorrListlist [numpy.array]

List of cross-correlations. These are expected to be square arrays.

namestr

Name for log messages.

Returns

meanXcorrnumpy.array, (N, N)

The averaged cross-correlation.

quadraticCorrelations(xCorrList, fluxList, name)

Measure a quadratic correlation model.

Parameters

xCorrListlist [numpy.array]

List of cross-correlations. These are expected to be square arrays.

fluxListnumpy.array, (Nflux,)

Associated list of fluxes.

namestr

Name for log messages.

Returns

meanXcorrnumpy.array, (N, N)

The averaged cross-correlation.

run(inputPtc, dummy, camera, inputDims)

Combine covariance information from PTC into brighter-fatter kernels.

Parameters

inputPtclsst.ip.isr.PhotonTransferCurveDataset

PTC data containing per-amplifier covariance measurements.

dummylsst.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.

cameralsst.afw.cameraGeom.Camera

Camera to use for camera geometry information.

inputDimslsst.daf.butler.DataCoordinate or dict

DataIds to use to populate the output calibration.

Returns

resultslsst.pipe.base.Struct

The resulst struct containing:

outputBfk

Resulting Brighter-Fatter Kernel (lsst.ip.isr.BrighterFatterKernel).

runQuantum(butlerQC, inputRefs, outputRefs)

Ensure that the input and output dimensions are passed along.

Parameters

butlerQClsst.daf.butler.QuantumContext

Butler to operate on.

inputRefslsst.pipe.base.InputQuantizedConnection

Input data refs to load.

ouptutRefslsst.pipe.base.OutputQuantizedConnection

Output data refs to persist.

sampleCovModel(fluxes, noiseMatrix, gain, covModelList, flux, name)

Sample the correlation model and measure widetile{C}_{ij} from Broughton et al. 2023 (eq. 4)

Parameters

fluxeslist [float]

List of fluxes (in ADU)

noiseMatrixnumpy.array, (N, N)

Noise matrix

gainfloat

Amplifier gain

covModelListnumpy.array, (N, N)

List of covariance model matrices. These are expected to be square arrays.

fluxfloat

Flux in electrons at which to sample the covariance model.

namestr

Name for log messages.

Returns

covTildenumpy.array, (N, N)

The calculated C-tilde from Broughton et al. 2023 (eq. 4).

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

sourcenumpy.ndarray, (N, N)

The input array.

maxIterint, optional

Maximum number of iterations to attempt before aborting.

eLevelfloat, optional

The target error level at which we deem convergence to have occurred.

Returns

outputnumpy.ndarray, (N, N)

The solution.