CpFlatNormalizationTask

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

Bases: lsst.pipe.base.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.
getAllSchemaCatalogs()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
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.
getResourceConfig()	Return resource configuration for this task.
getSchemaCatalogs()	Get the schemas generated by this 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)	Method to do butler IO and or transforms 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 = True

Methods Documentation

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs() → Dict[str, Any]

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:

schemacatalogs : dict

Keys are butler dataset type, values are a empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getFullMetadata() → lsst.pipe.base._task_metadata.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.

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”.

getName() → str

Get the name of the task.

Returns:

taskName : str

Name of the task.

See also

getFullName

getResourceConfig() → Optional[ResourceConfig]

Return resource configuration for this task.

Returns:

Object of type ResourceConfig or None if resource

configuration is not defined for this task.

getSchemaCatalogs() → Dict[str, Any]

Get the schemas generated by this task.

Returns:

schemaCatalogs : dict

Keys are butler dataset type, values are an empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for this task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

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.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:

doc : str

Help text for the field.

Returns:

configurableField : lsst.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) → 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”.

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:

bgMatrix : np.ndarray, (nDetectors, nExposures)

Input backgrounds indexed by exposure (axis=0) and detector (axis=1).

bgCounts : np.ndarray, (nDetectors, nExposures), optional

Input pixel counts used to in measuring bgMatrix, indexed identically.

iterations : int, optional

Number of iterations to use in decomposition.

Returns:

scaleResult : lsst.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:

inputMDs : list [lsst.daf.base.PropertyList]

Amplifier-level metadata used to construct scales.

inputDims : list [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:

results : lsst.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)

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQC : ButlerQuantumContext

A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.

inputRefs : InputQuantizedConnection

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.

outputRefs : OutputQuantizedConnection

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:

name : str

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time