CalibCombineTask

class lsst.cp.pipe.CalibCombineTask(**kwargs)

Bases: lsst.pipe.base.PipelineTask

Task to combine calib exposures.

Attributes Summary

canMultiprocess

Methods Summary

applyScale(exposure[, bbox, scale])	Apply scale to input exposure.
combine(target, expHandleList, expScaleList, …)	Combine multiple images.
combineHeaders(expHandleList, calib[, …])	Combine input headers to determine the set of common headers, supplemented by calibration inputs.
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.
getDimensions(expHandleList)	Get dimensions of the inputs.
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.
getSize(dimList)	Determine a consistent size, given a list of image sizes.
getTaskDict()	Get a dictionary of all tasks as a shallow copy.
interpolateNans(exp)	Interpolate over NANs in the combined image.
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.
run(inputExpHandles[, inputScales, inputDims])	Combine calib exposures for a single detector.
runQuantum(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
setFilter(exp, filterLabel)	Dummy function that will not assign a filter.
timer(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Attributes Documentation

canMultiprocess = True

Methods Documentation

applyScale(exposure, bbox=None, scale=None)

Apply scale to input exposure.

This implementation applies a flux scaling: the input exposure is divided by the provided scale.

Parameters:

exposure : lsst.afw.image.Exposure

Exposure to scale.

bbox : lsst.geom.Box2I

BBox matching the segment of the exposure passed in.

scale : float or list [float], optional

Constant scale to divide the exposure by.

combine(target, expHandleList, expScaleList, stats)

Combine multiple images.

Parameters:

target : lsst.afw.image.Exposure

Output exposure to construct.

expHandleList : list [lsst.daf.butler.DeferredDatasetHandle]

Input exposure handles to combine.

expScaleList : list [float]

List of scales to apply to each input image.

stats : lsst.afw.math.StatisticsControl

Control explaining how to combine the input images.

combineHeaders(expHandleList, calib, calibType='CALIB', scales=None)

Combine input headers to determine the set of common headers, supplemented by calibration inputs.

Parameters:

expHandleList : list [lsst.daf.butler.DeferredDatasetHandle]

Input list of exposure handles to combine.

calib : lsst.afw.image.Exposure

Output calibration to construct headers for.

calibType : str, optional

OBSTYPE the output should claim.

scales : list [float], optional

Scale values applied to each input to record.

Returns:

header : lsst.daf.base.PropertyList

Constructed header.

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.

getDimensions(expHandleList)

Get dimensions of the inputs.

Parameters:

expHandleList : list [lsst.daf.butler.DeferredDatasetHandle]

Exposure handles to check the sizes of.

Returns:

width, height : int

Unique set of input dimensions.

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.

getSize(dimList)

Determine a consistent size, given a list of image sizes.

Parameters:

dimList : list [tuple [int, int]]

List of dimensions.

Returns:

width, height : int

Common dimensions.

Raises:

RuntimeError

If input dimensions are inconsistent.

getTaskDict() → Dict[str, weakref]

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.

interpolateNans(exp)

Interpolate over NANs in the combined image.

NANs can result from masked areas on the CCD. We don’t want them getting into our science images, so we replace them with the median of the image.

Parameters:

exp : lsst.afw.image.Exposure

Exp to check for NaNs.

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.

run(inputExpHandles, inputScales=None, inputDims=None)

Combine calib exposures for a single detector.

Parameters:

inputExpHandles : list [lsst.daf.butler.DeferredDatasetHandle]

Input list of exposure handles to combine.

inputScales : dict [dict [dict [float]]], optional

Dictionary of scales, indexed by detector (int), amplifier (int), and exposure (int). Used for ‘inputExps’ scaling.

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:

outputData

Final combined exposure generated from the inputs (lsst.afw.image.Exposure).

Raises:

RuntimeError

Raised if no input data is found. Also raised if config.exposureScaling == InputList, and a necessary scale was not found.

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.

static setFilter(exp, filterLabel)

Dummy function that will not assign a filter.

Parameters:

exp : lsst.afw.image.Exposure

Exposure to assign filter to.

filterLabel : lsst.afw.image.FilterLabel

Filter to assign.

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