SkyMeasurementTask

class lsst.pipe.tasks.background.SkyMeasurementTask(config: Config | None = None, *, name: str | None = None, parentTask: Task | None = None, log: logging.Logger | lsst.utils.logging.LsstLogAdapter | None = None)

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
exposureToBackground(bgExp)	Convert an exposure to background model
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

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.

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

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