SkyMeasurementTask¶

Bases: Task

Task for creating, persisting and using sky frames

A sky frame is like a fringe frame (the sum of many exposures of the night sky, combined with rejection to remove astrophysical objects) except the structure is on larger scales, and hence we bin the images and represent them as a background model (a lsst.afw.math.BackgroundMI). The sky frame represents the dominant response of the camera to the sky background.

Methods Summary

`averageBackgrounds`(bgList)	Average multiple background models
`backgroundToExposure`(statsImage, bbox)	Convert a background model to an exposure
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`exposureToBackground`(bgExp)	Convert an exposure to background model
`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.
`measureBackground`(image)	Measure a background model for image
`measureScale`(image, skyBackground)	Measure scale of background model in image
`solveScales`(scales)	Solve multiple scales for a single scale factor
`subtractSkyFrame`(image, skyBackground, scale)	Subtract sky frame from science image
`timer`(name[, logLevel])	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

averageBackgrounds(bgList)¶

Average multiple background models

The input background models should be a BackgroundList consisting of a single BackgroundMI.

Parameters:

bgListlist of lsst.afw.math.BackgroundList: Background models to average.

Returns:

bgExplsst.afw.image.Exposure: Background model in Exposure format.

backgroundToExposure(statsImage, bbox)¶

Convert a background model to an exposure

Calibs need to be persisted as an Exposure, so we need to convert the background model to an Exposure.

Parameters:

statsImagelsst.afw.image.MaskedImageF: Background model’s statistics image.
bboxlsst.geom.Box2I: Bounding box for image.

Returns:

explsst.afw.image.Exposure: Background model in Exposure format.

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

static exposureToBackground(bgExp)¶

Convert an exposure to background model

Calibs need to be persisted as an Exposure, so we need to convert the persisted Exposure to a background model.

Parameters:

bgExplsst.afw.image.Exposure: Background model in Exposure format.

Returns:

bglsst.afw.math.BackgroundList: Background model

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

getTaskDict() → Dict[str, ReferenceType[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.

measureBackground(image)¶

Measure a background model for image

This doesn’t use a full-featured background model (e.g., no Chebyshev approximation) because we just want the binning behaviour. This will allow us to average the bins later (averageBackgrounds).

The BackgroundMI is wrapped in a BackgroundList so it can be pickled and persisted.

Parameters:

imagelsst.afw.image.MaskedImage: Image for which to measure background.

Returns:

bgModellsst.afw.math.BackgroundList: Background model.

measureScale(image, skyBackground)¶

Measure scale of background model in image

We treat the sky frame much as we would a fringe frame (except the length scale of the variations is different): we measure samples on the input image and the sky frame, which we will use to determine the scaling factor in the ‘solveScales` method.

Parameters:

imagelsst.afw.image.Exposure or lsst.afw.image.MaskedImage: Science image for which to measure scale.
skyBackgroundlsst.afw.math.BackgroundList: Sky background model.

Returns:

imageSamplesnumpy.ndarray: Sample measurements on image.
skySamplesnumpy.ndarray: Sample measurements on sky frame.

solveScales(scales)¶

Solve multiple scales for a single scale factor

Having measured samples from the image and sky frame, we fit for the scaling factor.

Parameters:

scaleslist of a tuple of two numpy.ndarray arrays: A list of the results from measureScale method.

Returns:

scalefloat: Scale factor.

subtractSkyFrame(image, skyBackground, scale, bgList=None)¶

Subtract sky frame from science image

Parameters:

imagelsst.afw.image.Exposure or lsst.afw.image.MaskedImage: Science image.
skyBackgroundlsst.afw.math.BackgroundList: Sky background model.
scalefloat: Scale to apply to background model.
bgListlsst.afw.math.BackgroundList: List of backgrounds applied to image

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.
logLevel: A logging level constant.

Navigation

SkyMeasurementTask¶