CpFlatNormalizationTask

CpFlatNormalizationTask determines the scaling factor to apply to each exposure/detector set when constructing the final flat field.

Processing summary

CpFlatNormalizationTask runs these operations:

1. Combine the set of background measurements for all input exposures for all detectors into a matrix B[exposure, detector].
2. Iteratively solve for two vectors E[exposure] and G[detector] whose Cartesian product are the best fit to B[exposure, detector].

Python API summary

from lsst.cp.pipe.cpFlatNormTask import CpFlatNormalizationTask

classCpFlatNormalizationTask(*, config: Optional[PipelineTaskConfig] = None, log: Optional[Union[logging.Logger, LsstLogAdapter]] = None, initInputs: Optional[Dict[str, Any]] = None, **kwargs)

Rescale merged flat frames to remove unequal screen illumination...

attributeconfig

Access configuration fields and retargetable subtasks.

methodrun(inputMDs, inputDims, camera)

Normalize FLAT exposures to a consistent level...

See also

See the CpFlatNormalizationTask API reference for complete details.

Retargetable subtasks

No subtasks.

Configuration fields

connections

Data type

lsst.pipe.base.config.Connections

Field type

ConfigField

Configurations describing the connections of the PipelineTask to datatypes

level

Default

'DETECTOR'

Field type

str ChoiceField (optional)

Choices

'DETECTOR'

Correct using full detector statistics.

'AMP'

Correct using individual amplifiers.

None

Field is optional

Which level to apply normalizations.

saveLogOutput

Default

True

Field type

bool Field

Flag to enable/disable saving of log output for a task, enabled by default.

saveMetadata

Default

True

Field type

bool Field

Flag to enable/disable metadata saving for a task, enabled by default.

scaleMaxIter

Default

10

Field type

int Field

Max number of iterations to use in scale solver.