lsst.pipe.drivers  21.0.0-5-gd00fb1e+277babe5d7
Public Member Functions | Static Public Member Functions | Static Public Attributes | List of all members
lsst.pipe.drivers.background.SkyMeasurementTask Class Reference
Inheritance diagram for lsst.pipe.drivers.background.SkyMeasurementTask:

Public Member Functions

def getSkyData (self, butler, calibId)
 
def backgroundToExposure (self, statsImage, bbox)
 
def measureBackground (self, image)
 
def averageBackgrounds (self, bgList)
 
def measureScale (self, image, skyBackground)
 
def solveScales (self, scales)
 
def subtractSkyFrame (self, image, skyBackground, scale, bgList=None)
 

Static Public Member Functions

def exposureToBackground (bgExp)
 

Static Public Attributes

 ConfigClass = SkyMeasurementConfig
 

Detailed Description

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.

Definition at line 82 of file background.py.

Member Function Documentation

◆ averageBackgrounds()

def lsst.pipe.drivers.background.SkyMeasurementTask.averageBackgrounds (   self,
  bgList 
)
Average multiple background models

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

Parameters
----------
bgList : `list` of `lsst.afw.math.BackgroundList`
    Background models to average.

Returns
-------
bgExp : `lsst.afw.image.Exposure`
    Background model in Exposure format.

Definition at line 211 of file background.py.

◆ backgroundToExposure()

def lsst.pipe.drivers.background.SkyMeasurementTask.backgroundToExposure (   self,
  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
----------
statsImage : `lsst.afw.image.MaskedImageF`
    Background model's statistics image.
bbox : `lsst.geom.Box2I`
    Bounding box for image.

Returns
-------
exp : `lsst.afw.image.Exposure`
    Background model in Exposure format.

Definition at line 142 of file background.py.

◆ exposureToBackground()

def lsst.pipe.drivers.background.SkyMeasurementTask.exposureToBackground (   bgExp)
static
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
----------
bgExp : `lsst.afw.image.Exposure`
    Background model in Exposure format.

Returns
-------
bg : `lsst.afw.math.BackgroundList`
    Background model

Definition at line 112 of file background.py.

◆ getSkyData()

def lsst.pipe.drivers.background.SkyMeasurementTask.getSkyData (   self,
  butler,
  calibId 
)
Retrieve sky frame from the butler

Parameters
----------
butler : `lsst.daf.persistence.Butler`
    Data butler
calibId : `dict`
    Data identifier for calib

Returns
-------
sky : `lsst.afw.math.BackgroundList`
    Sky frame

Definition at line 93 of file background.py.

◆ measureBackground()

def lsst.pipe.drivers.background.SkyMeasurementTask.measureBackground (   self,
  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
----------
image : `lsst.afw.image.MaskedImage`
    Image for which to measure background.

Returns
-------
bgModel : `lsst.afw.math.BackgroundList`
    Background model.

Definition at line 169 of file background.py.

◆ measureScale()

def lsst.pipe.drivers.background.SkyMeasurementTask.measureScale (   self,
  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
----------
image : `lsst.afw.image.Exposure` or `lsst.afw.image.MaskedImage`
    Science image for which to measure scale.
skyBackground : `lsst.afw.math.BackgroundList`
    Sky background model.

Returns
-------
imageSamples : `numpy.ndarray`
    Sample measurements on image.
skySamples : `numpy.ndarray`
    Sample measurements on sky frame.

Definition at line 257 of file background.py.

◆ solveScales()

def lsst.pipe.drivers.background.SkyMeasurementTask.solveScales (   self,
  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
----------
scales : `list` of a `tuple` of two `numpy.ndarray` arrays
    A `list` of the results from `measureScale` method.

Returns
-------
scale : `float`
    Scale factor.

Definition at line 304 of file background.py.

◆ subtractSkyFrame()

def lsst.pipe.drivers.background.SkyMeasurementTask.subtractSkyFrame (   self,
  image,
  skyBackground,
  scale,
  bgList = None 
)
Subtract sky frame from science image

Parameters
----------
image : `lsst.afw.image.Exposure` or `lsst.afw.image.MaskedImage`
    Science image.
skyBackground : `lsst.afw.math.BackgroundList`
    Sky background model.
scale : `float`
    Scale to apply to background model.
bgList : `lsst.afw.math.BackgroundList`
    List of backgrounds applied to image

Definition at line 346 of file background.py.

Member Data Documentation

◆ ConfigClass

lsst.pipe.drivers.background.SkyMeasurementTask.ConfigClass = SkyMeasurementConfig
static

Definition at line 91 of file background.py.


The documentation for this class was generated from the following file: