CpFlatNormalizationTask¶

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

Bases: PipelineTask

Rescale merged flat frames to remove unequal screen illumination.

Attributes Summary

canMultiprocess

Methods Summary

`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.
`getName`()	Get the name of the task.
`getTaskDict`()	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.
`measureScales`(bgMatrix[, bgCounts, iterations])	Convert backgrounds to exposure and detector components.
`run`(inputMDs, inputDims, camera)	Normalize FLAT exposures to a consistent level.
`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

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”.

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.

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.

measureScales(bgMatrix, bgCounts=None, iterations=10)¶

Convert backgrounds to exposure and detector components.

Parameters:

bgMatrixnp.ndarray, (nDetectors, nExposures): Input backgrounds indexed by exposure (axis=0) and detector (axis=1).
bgCountsnp.ndarray, (nDetectors, nExposures), optional: Input pixel counts used to in measuring bgMatrix, indexed identically.
iterationsint, optional: Number of iterations to use in decomposition.

Returns:

scaleResultlsst.pipe.base.Struct

Result struct containing fields:

vectorE: Output E vector of exposure level scalings (np.array, (nExposures)).
vectorG: Output G vector of detector level scalings (np.array, (nExposures)).
bgModel: Expected model bgMatrix values, calculated from E and G (np.ndarray, (nDetectors, nExposures)).

Notes

The set of background measurements B[exposure, detector] of flat frame data should be defined by a “Cartesian” product of two vectors, E[exposure] and G[detector]. The E vector represents the total flux incident on the focal plane. In a perfect camera, this is simply the sum along the columns of B (np.sum(B, axis=0)).

However, this simple model ignores differences in detector gains, the vignetting of the detectors, and the illumination pattern of the source lamp. The G vector describes these detector dependent differences, which should be identical over different exposures. For a perfect lamp of unit total intensity, this is simply the sum along the rows of B (np.sum(B, axis=1)). This algorithm divides G by the total flux level, to provide the relative (not absolute) scales between detectors.

The algorithm here, from pipe_drivers/constructCalibs.py and from there from Eugene Magnier/PanSTARRS [1], attempts to iteratively solve this decomposition from initial “perfect” E and G vectors. The operation is performed in log space to reduce the multiply and divides to linear additions and subtractions.

References

[1]

https://svn.pan-starrs.ifa.hawaii.edu/trac/ipp/browser/trunk/psModules/src/detrend/pmFlatNormalize.c # noqa: W505, E501

run(inputMDs, inputDims, camera)¶

Normalize FLAT exposures to a consistent level.

Parameters:

inputMDslist [lsst.daf.base.PropertyList]

Amplifier-level metadata used to construct scales.

inputDimslist [dict]

List of dictionaries of input data dimensions/values. Each list entry should contain:

"exposure": exposure id value (int)
"detector": detector id value (int)

Returns:

resultslsst.pipe.base.Struct

The results struct containing:

outputScales: Dictionary of scales, indexed by detector (int), amplifier (int), and exposure (int) (dict [dict [dict [float]]]).

Raises:

KeyError: Raised if the input dimensions do not contain detector and exposure, or if the metadata does not contain the expected statistic entry.

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

CpFlatNormalizationTask¶