def 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
"""
if isinstance(image, afwImage.Exposure):
image = image.getMaskedImage()
if isinstance(image, afwImage.MaskedImage):
image = image.getImage()
image.scaledMinus(scale, skyBackground.getImage())
if bgList is not None:
# Append the sky frame to the list of applied background models
bgData = list(skyBackground[0])
bg = bgData[0]
statsImage = bg.getStatsImage().clone()
statsImage *= scale
newBg = afwMath.BackgroundMI(bg.getImageBBox(), statsImage)
newBgData = [newBg] + bgData[1:]
bgList.append(newBgData)
def 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
----------
bgExp : `lsst.afw.image.Exposure`
Background model in Exposure format.
Returns
-------
bg : `lsst.afw.math.BackgroundList`
Background model
"""
header = bgExp.getMetadata()
xMin = header.getScalar("BOX.MINX")
yMin = header.getScalar("BOX.MINY")
xMax = header.getScalar("BOX.MAXX")
yMax = header.getScalar("BOX.MAXY")
algorithm = header.getScalar("ALGORITHM")
bbox = afwGeom.Box2I(afwGeom.Point2I(xMin, yMin), afwGeom.Point2I(xMax, yMax))
return afwMath.BackgroundList(
(afwMath.BackgroundMI(bbox, bgExp.getMaskedImage()),
afwMath.stringToInterpStyle(algorithm),
afwMath.stringToUndersampleStyle("REDUCE_INTERP_ORDER"),
afwMath.ApproximateControl.UNKNOWN,
0, 0, False))
def toCcdBackground(self, detector, bbox):
"""Produce a background model for a CCD
The superpixel background model is warped back to the
CCD frame, for application to the individual CCD.
Parameters
----------
detector : `lsst.afw.cameraGeom.Detector`
CCD for which to produce background model.
bbox : `lsst.geom.Box2I`
Bounding box of CCD exposure.
Returns
-------
bg : `lsst.afw.math.BackgroundList`
Background model for CCD.
"""
transform = detector.getTransformMap().getTransform(
detector.makeCameraSys(afwCameraGeom.PIXELS),
detector.makeCameraSys(afwCameraGeom.FOCAL_PLANE))
binTransform = (
geom.AffineTransform.makeScaling(self.config.binning) *
geom.AffineTransform.makeTranslation(geom.Extent2D(0.5, 0.5)))
# Binned image on CCD --> unbinned image on CCD --> focal plane --> binned focal plane
toSample = afwGeom.makeTransform(binTransform).then(transform).then(
self.transform)
focalPlane = self.getStatsImage()
fpNorm = afwImage.ImageF(focalPlane.getBBox())
fpNorm.set(1.0)
image = afwImage.ImageF(bbox.getDimensions() // self.config.binning)
norm = afwImage.ImageF(image.getBBox())
ctrl = afwMath.WarpingControl("bilinear")
afwMath.warpImage(image, focalPlane, toSample.inverted(), ctrl)
afwMath.warpImage(norm, fpNorm, toSample.inverted(), ctrl)
image /= norm
mask = afwImage.Mask(image.getBBox())
isBad = numpy.isnan(image.getArray())
mask.getArray()[isBad] = mask.getPlaneBitMask("BAD")
image.getArray()[isBad] = image.getArray()[~isBad].mean()
return afwMath.BackgroundList(
(afwMath.BackgroundMI(bbox, afwImage.makeMaskedImage(image, mask)),
afwMath.stringToInterpStyle(self.config.interpolation),
afwMath.stringToUndersampleStyle("REDUCE_INTERP_ORDER"),
afwMath.ApproximateControl.UNKNOWN, 0, 0, False))
def run(self,
exposure,
exposureIdInfo=None,
background=None,
icSourceCat=None):
"""Produce calibration outputs with no processing.
Parameters
----------
exposure : `lsst.afw.image.Exposure`
Exposure to calibrate.
exposureIdInfo : `lsst.obs.base.ExposureIdInfo`
ID info for exposure.
background : `lsst.afw.math.BackgroundList`
Background model already subtracted from exposure.
icSourceCat : `lsst.afw.table.SourceCatalog`
A SourceCatalog from CharacterizeImageTask from which we can copy some fields.
Returns
-------
result : `lsst.pipe.base.Struct`
Struct containing these fields:
``outputExposure``
Calibrated science exposure with refined WCS and PhotoCalib
(`lsst.afw.image.Exposure`).
``outputBackground``
Model of background subtracted from exposure
(`lsst.afw.math.BackgroundList`).
``outputCat``
Catalog of measured sources (`lsst.afw.table.SourceCatalog`).
``astromMatches``
List of source/refObj matches from the astrometry solver
(`list` [`lsst.afw.table.ReferenceMatch`]).
"""
# Can't persist empty BackgroundList; DM-33714
bg = afwMath.BackgroundMI(
geom.Box2I(geom.Point2I(0, 0), geom.Point2I(16, 16)),
afwImage.MaskedImageF(16, 16))
return Struct(
outputExposure=exposure,
outputBackground=afwMath.BackgroundList(bg),
outputCat=afwTable.SourceCatalog(),
astromMatches=[],
)
def testBackgroundFromStatsImage(self):
"""Check that we can rebuild a Background from a BackgroundMI.getStatsImage()"""
bgCtrl = afwMath.BackgroundControl(10, 10)
bkgd = afwMath.makeBackground(self.image, bgCtrl)
interpStyle = afwMath.Interpolate.AKIMA_SPLINE
undersampleStyle = afwMath.REDUCE_INTERP_ORDER
bkgdImage = bkgd.getImageF(interpStyle, undersampleStyle)
self.assertEqual(np.mean(bkgdImage.getArray()), self.val)
self.assertEqual(interpStyle, bkgd.getAsUsedInterpStyle())
self.assertEqual(undersampleStyle, bkgd.getAsUsedUndersampleStyle())
# OK, we have our background. Make a copy
bkgd2 = afwMath.BackgroundMI(self.image.getBBox(),
bkgd.getStatsImage())
del bkgd # we should be handling the memory correctly, but let's check
bkgdImage2 = bkgd2.getImageF(interpStyle)
self.assertEqual(np.mean(bkgdImage2.getArray()), self.val)
def run(self, exposure, exposureIdInfo=None, background=None):
"""Produce characterization outputs with no processing.
Parameters
----------
exposure : `lsst.afw.image.Exposure`
Exposure to characterize.
exposureIdInfo : `lsst.obs.base.ExposureIdInfo`
ID info for exposure.
background : `lsst.afw.math.BackgroundList`
Initial model of background already subtracted from exposure.
Returns
-------
result : `lsst.pipe.base.Struct`
Struct containing these fields:
``characterized``
Characterized exposure (`lsst.afw.image.Exposure`).
``sourceCat``
Detected sources (`lsst.afw.table.SourceCatalog`).
``backgroundModel``
Model of background subtracted from exposure (`lsst.afw.math.BackgroundList`)
``psfCellSet``
Spatial cells of PSF candidates (`lsst.afw.math.SpatialCellSet`)
"""
# Can't persist empty BackgroundList; DM-33714
bg = afwMath.BackgroundMI(
geom.Box2I(geom.Point2I(0, 0), geom.Point2I(16, 16)),
afwImage.MaskedImageF(16, 16))
return Struct(
characterized=exposure,
sourceCat=afwTable.SourceCatalog(),
backgroundModel=afwMath.BackgroundList(bg),
psfCellSet=afwMath.SpatialCellSet(exposure.getBBox(), 10),
)