def showPsfCandidates(exposure, psfCellSet, psf=None, frame=None, normalize=True, showBadCandidates=True,
variance=None, chi=None):
"""Display the PSF candidates.
If psf is provided include PSF model and residuals; if normalize is true normalize the PSFs (and residuals)
If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi
"""
if chi is None:
if variance is not None: # old name for chi
chi = variance
#
# Show us the ccandidates
#
mos = displayUtils.Mosaic()
#
candidateCenters = []
candidateCentersBad = []
candidateIndex = 0
for cell in psfCellSet.getCellList():
for cand in cell.begin(False): # include bad candidates
cand = algorithmsLib.cast_PsfCandidateF(cand)
rchi2 = cand.getChi2()
if rchi2 > 1e100:
rchi2 = numpy.nan
if not showBadCandidates and cand.isBad():
continue
if psf:
im_resid = displayUtils.Mosaic(gutter=0, background=-5, mode="x")
try:
im = cand.getMaskedImage() # copy of this object's image
xc, yc = cand.getXCenter(), cand.getYCenter()
margin = 0 if True else 5
w, h = im.getDimensions()
bbox = afwGeom.BoxI(afwGeom.PointI(margin, margin), im.getDimensions())
if margin > 0:
bim = im.Factory(w + 2*margin, h + 2*margin)
stdev = numpy.sqrt(afwMath.makeStatistics(im.getVariance(), afwMath.MEAN).getValue())
afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
bim *= stdev
var = bim.getVariance(); var.set(stdev**2); del var
sbim = im.Factory(bim, bbox)
sbim <<= im
del sbim
im = bim
xc += margin; yc += margin
im = im.Factory(im, True)
im.setXY0(cand.getMaskedImage().getXY0())
except:
continue
if not variance:
im_resid.append(im.Factory(im, True))
# residuals using spatial model
chi2 = algorithmsLib.subtractPsf(psf, im, xc, yc)
resid = im
if variance:
resid = resid.getImage()
var = im.getVariance()
var = var.Factory(var, True)
numpy.sqrt(var.getArray(), var.getArray()) # inplace sqrt
resid /= var
im_resid.append(resid)
# Fit the PSF components directly to the data (i.e. ignoring the spatial model)
im = cand.getMaskedImage()
im = im.Factory(im, True)
im.setXY0(cand.getMaskedImage().getXY0())
noSpatialKernel = afwMath.cast_LinearCombinationKernel(psf.getKernel())
candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
fit = algorithmsLib.fitKernelParamsToImage(noSpatialKernel, im, candCenter)
params = fit[0]
kernels = afwMath.KernelList(fit[1])
outputKernel = afwMath.LinearCombinationKernel(kernels, params)
outImage = afwImage.ImageD(outputKernel.getDimensions())
outputKernel.computeImage(outImage, False)
im -= outImage.convertF()
resid = im
if margin > 0:
bim = im.Factory(w + 2*margin, h + 2*margin)
afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
bim *= stdev
sbim = im.Factory(bim, bbox)
sbim <<= resid
del sbim
resid = bim
if variance:
resid = resid.getImage()
resid /= var
im_resid.append(resid)
im = im_resid.makeMosaic()
else:
im = cand.getMaskedImage()
if normalize:
im /= afwMath.makeStatistics(im, afwMath.MAX).getValue()
objId = splitId(cand.getSource().getId(), True)["objId"]
if psf:
lab = "%d chi^2 %.1f" % (objId, rchi2)
ctype = ds9.RED if cand.isBad() else ds9.GREEN
else:
lab = "%d flux %8.3g" % (objId, cand.getSource().getPsfFlux())
ctype = ds9.GREEN
mos.append(im, lab, ctype)
if False and numpy.isnan(rchi2):
ds9.mtv(cand.getMaskedImage().getImage(), title="candidate", frame=1)
print "amp", cand.getAmplitude()
im = cand.getMaskedImage()
center = (candidateIndex, xc - im.getX0(), yc - im.getY0())
candidateIndex += 1
if cand.isBad():
candidateCentersBad.append(center)
else:
candidateCenters.append(center)
if variance:
title = "chi(Psf fit)"
else:
title = "Stars & residuals"
mosaicImage = mos.makeMosaic(frame=frame, title=title)
with ds9.Buffering():
for centers, color in ((candidateCenters, ds9.GREEN), (candidateCentersBad, ds9.RED)):
for cen in centers:
bbox = mos.getBBox(cen[0])
ds9.dot("+", cen[1] + bbox.getMinX(), cen[2] + bbox.getMinY(), frame=frame, ctype=color)
return mosaicImage
def showPsfMosaic(exposure, psf=None, nx=7, ny=None,
showCenter=True, showEllipticity=False, showFwhm=False,
stampSize=0, frame=None, title=None):
"""Show a mosaic of Psf images. exposure may be an Exposure (optionally with PSF),
or a tuple (width, height)
If stampSize is > 0, the psf images will be trimmed to stampSize*stampSize
"""
scale = 1.0
if showFwhm:
showEllipticity = True
scale = 2*math.log(2) # convert sigma^2 to HWHM^2 for a Gaussian
mos = displayUtils.Mosaic()
try: # maybe it's a real Exposure
width, height = exposure.getWidth(), exposure.getHeight()
x0, y0 = exposure.getXY0()
if not psf:
psf = exposure.getPsf()
except AttributeError:
try: # OK, maybe a list [width, height]
width, height = exposure[0], exposure[1]
x0, y0 = 0, 0
except TypeError: # I guess not
raise RuntimeError("Unable to extract width/height from object of type %s" % type(exposure))
if not ny:
ny = int(nx*float(height)/width + 0.5)
if not ny:
ny = 1
centroidName = "base_GaussianCentroid"
shapeName = "base_SdssShape"
schema = afwTable.SourceTable.makeMinimalSchema()
schema.getAliasMap().set("slot_Centroid", centroidName)
schema.getAliasMap().set("slot_Centroid_flag", centroidName+"_flag")
control = measBase.GaussianCentroidControl()
centroider = measBase.GaussianCentroidAlgorithm(control, centroidName, schema)
sdssShape = measBase.SdssShapeControl()
shaper = measBase.SdssShapeAlgorithm(sdssShape, shapeName, schema)
table = afwTable.SourceTable.make(schema)
table.defineCentroid(centroidName)
table.defineShape(shapeName)
bbox = None
if stampSize > 0:
w, h = psf.computeImage(afwGeom.PointD(0, 0)).getDimensions()
if stampSize <= w and stampSize <= h:
bbox = afwGeom.BoxI(afwGeom.PointI((w - stampSize)//2, (h - stampSize)//2),
afwGeom.ExtentI(stampSize, stampSize))
centers = []
shapes = []
for iy in range(ny):
for ix in range(nx):
x = int(ix*(width-1)/(nx-1)) + x0
y = int(iy*(height-1)/(ny-1)) + y0
im = psf.computeImage(afwGeom.PointD(x, y)).convertF()
imPeak = psf.computePeak(afwGeom.PointD(x, y))
im /= imPeak
if bbox:
im = im.Factory(im, bbox)
lab = "PSF(%d,%d)" % (x, y) if False else ""
mos.append(im, lab)
exp = afwImage.makeExposure(afwImage.makeMaskedImage(im))
w, h = im.getWidth(), im.getHeight()
centerX = im.getX0() + w//2
centerY = im.getY0() + h//2
src = table.makeRecord()
foot = afwDet.Footprint(exp.getBBox())
foot.addPeak(centerX, centerY, 1)
src.setFootprint(foot)
centroider.measure(src, exp)
centers.append((src.getX() - im.getX0(), src.getY() - im.getY0()))
shaper.measure(src, exp)
shapes.append((src.getIxx(), src.getIxy(), src.getIyy()))
mos.makeMosaic(frame=frame, title=title if title else "Model Psf", mode=nx)
if centers and frame is not None:
with ds9.Buffering():
for i, (cen, shape) in enumerate(zip(centers, shapes)):
bbox = mos.getBBox(i)
xc, yc = cen[0] + bbox.getMinX(), cen[1] + bbox.getMinY()
if showCenter:
ds9.dot("+", xc, yc, ctype=ds9.BLUE, frame=frame)
if showEllipticity:
ixx, ixy, iyy = shape
ixx *= scale
ixy *= scale
iyy *= scale
ds9.dot("@:%g,%g,%g" % (ixx, ixy, iyy), xc, yc, frame=frame, ctype=ds9.RED)
return mos
def getClumps(self, sigma=1.0, display=False):
if self._num <= 0:
raise RuntimeError("No candidate PSF sources")
psfImage = self.getImage()
#
# Embed psfImage into a larger image so we can smooth when measuring it
#
width, height = psfImage.getWidth(), psfImage.getHeight()
largeImg = psfImage.Factory(afwGeom.ExtentI(2 * width, 2 * height))
largeImg.set(0)
bbox = afwGeom.BoxI(afwGeom.PointI(width, height),
afwGeom.ExtentI(width, height))
largeImg.assign(psfImage, bbox, afwImage.LOCAL)
#
# Now measure that image, looking for the highest peak. Start by building an Exposure
#
msk = afwImage.MaskU(largeImg.getDimensions())
msk.set(0)
var = afwImage.ImageF(largeImg.getDimensions())
var.set(1)
mpsfImage = afwImage.MaskedImageF(largeImg, msk, var)
mpsfImage.setXY0(afwGeom.PointI(-width, -height))
del msk
del var
exposure = afwImage.makeExposure(mpsfImage)
#
# Next run an object detector
#
maxVal = afwMath.makeStatistics(psfImage, afwMath.MAX).getValue()
threshold = maxVal - sigma * math.sqrt(maxVal)
if threshold <= 0.0:
threshold = maxVal
threshold = afwDetection.Threshold(threshold)
ds = afwDetection.FootprintSet(mpsfImage, threshold, "DETECTED")
#
# And measure it. This policy isn't the one we use to measure
# Sources, it's only used to characterize this PSF histogram
#
schema = SourceTable.makeMinimalSchema()
psfImageConfig = SingleFrameMeasurementConfig()
psfImageConfig.slots.centroid = "base_SdssCentroid"
psfImageConfig.plugins["base_SdssCentroid"].doFootprintCheck = False
psfImageConfig.slots.psfFlux = None # "base_PsfFlux"
psfImageConfig.slots.apFlux = "base_CircularApertureFlux_3_0"
psfImageConfig.slots.modelFlux = None
psfImageConfig.slots.instFlux = None
psfImageConfig.slots.calibFlux = None
psfImageConfig.slots.shape = "base_SdssShape"
# Formerly, this code had centroid.sdss, flux.psf, flux.naive,
# flags.pixel, and shape.sdss
psfImageConfig.algorithms.names = [
"base_SdssCentroid", "base_CircularApertureFlux", "base_SdssShape"
]
psfImageConfig.algorithms["base_CircularApertureFlux"].radii = [3.0]
psfImageConfig.validate()
task = SingleFrameMeasurementTask(schema, config=psfImageConfig)
sourceCat = SourceCatalog(schema)
gaussianWidth = 1.5 # Gaussian sigma for detection convolution
exposure.setPsf(algorithmsLib.DoubleGaussianPsf(11, 11, gaussianWidth))
ds.makeSources(sourceCat)
#
# Show us the Histogram
#
if display:
frame = 1
dispImage = mpsfImage.Factory(
mpsfImage,
afwGeom.BoxI(afwGeom.PointI(width, height),
afwGeom.ExtentI(width, height)), afwImage.LOCAL)
ds9.mtv(dispImage, title="PSF Selection Image", frame=frame)
clumps = list() # List of clumps, to return
e = None # thrown exception
IzzMin = 1.0 # Minimum value for second moments
IzzMax = (
self._xSize /
8.0)**2 # Max value ... clump radius should be < clumpImgSize/8
apFluxes = []
task.run(
sourceCat,
exposure) # notes that this is backwards for the new framework
for i, source in enumerate(sourceCat):
if source.getCentroidFlag():
continue
x, y = source.getX(), source.getY()
apFluxes.append(source.getApFlux())
val = mpsfImage.getImage().get(int(x) + width, int(y) + height)
psfClumpIxx = source.getIxx()
psfClumpIxy = source.getIxy()
psfClumpIyy = source.getIyy()
if display:
if i == 0:
ds9.pan(x, y, frame=frame)
ds9.dot("+", x, y, ctype=ds9.YELLOW, frame=frame)
ds9.dot("@:%g,%g,%g" % (psfClumpIxx, psfClumpIxy, psfClumpIyy),
x,
y,
ctype=ds9.YELLOW,
frame=frame)
if psfClumpIxx < IzzMin or psfClumpIyy < IzzMin:
psfClumpIxx = max(psfClumpIxx, IzzMin)
psfClumpIyy = max(psfClumpIyy, IzzMin)
if display:
ds9.dot("@:%g,%g,%g" %
(psfClumpIxx, psfClumpIxy, psfClumpIyy),
x,
y,
ctype=ds9.RED,
frame=frame)
det = psfClumpIxx * psfClumpIyy - psfClumpIxy * psfClumpIxy
try:
a, b, c = psfClumpIyy / det, -psfClumpIxy / det, psfClumpIxx / det
except ZeroDivisionError:
a, b, c = 1e4, 0, 1e4
clumps.append(
Clump(peak=val,
x=x,
y=y,
a=a,
b=b,
c=c,
ixx=psfClumpIxx,
ixy=psfClumpIxy,
iyy=psfClumpIyy))
if len(clumps) == 0:
msg = "Failed to determine center of PSF clump"
if e:
msg += ": %s" % e
raise RuntimeError(msg)
# if it's all we got return it
if len(clumps) == 1:
return clumps
# which clump is the best?
# if we've undistorted the moments, stars should only have 1 clump
# use the apFlux from the clump measurement, and take the highest
# ... this clump has more psf star candidate neighbours than the others.
# get rid of any that are huge, and thus poorly defined
goodClumps = []
for clump in clumps:
if clump.ixx < IzzMax and clump.iyy < IzzMax:
goodClumps.append(clump)
# if culling > IzzMax cost us all clumps, we'll have to take what we have
if len(goodClumps) == 0:
goodClumps = clumps
# use the 'brightest' clump
iBestClump = numpy.argsort(apFluxes)[0]
clumps = [clumps[iBestClump]]
return clumps
def _makeAmpInfoCatalog(self, ccd):
"""Construct an amplifier info catalog
"""
# Much of this will need to be filled in when we know it.
assert len(ccd['amplifiers']) > 0
amp = list(ccd['amplifiers'].values())[0]
rawBBox = self._makeBBoxFromList(amp['rawBBox']) # total in file
xRawExtent, yRawExtent = rawBBox.getDimensions()
from lsst.afw.table import LL, LR, UL, UR
readCorners = dict(LL=LL, LR=LR, UL=UL, UR=UR)
schema = AmpInfoTable.makeMinimalSchema()
linThreshKey = schema.addField('linearityThreshold', type=float)
linMaxKey = schema.addField('linearityMaximum', type=float)
linUnitsKey = schema.addField('linearityUnits', type=str, size=9)
hduKey = schema.addField('hdu', type=np.int32)
# end placeholder
self.ampInfoDict = {}
ampCatalog = AmpInfoCatalog(schema)
for name, amp in sorted(ccd['amplifiers'].items(),
key=lambda x: x[1]['hdu']):
record = ampCatalog.addNew()
record.setName(name)
record.set(hduKey, amp['hdu'])
ix, iy = amp['ixy']
perAmpData = amp['perAmpData']
if perAmpData:
x0, y0 = 0, 0 # origin of data within each amp image
else:
x0, y0 = ix * xRawExtent, iy * yRawExtent
rawDataBBox = self._makeBBoxFromList(
amp['rawDataBBox']) # Photosensitive area
xDataExtent, yDataExtent = rawDataBBox.getDimensions()
record.setBBox(
afwGeom.BoxI(
afwGeom.PointI(ix * xDataExtent, iy * yDataExtent),
rawDataBBox.getDimensions()))
rawBBox = self._makeBBoxFromList(amp['rawBBox'])
rawBBox.shift(afwGeom.ExtentI(x0, y0))
record.setRawBBox(rawBBox)
rawDataBBox = self._makeBBoxFromList(amp['rawDataBBox'])
rawDataBBox.shift(afwGeom.ExtentI(x0, y0))
record.setRawDataBBox(rawDataBBox)
rawSerialOverscanBBox = self._makeBBoxFromList(
amp['rawSerialOverscanBBox'])
rawSerialOverscanBBox.shift(afwGeom.ExtentI(x0, y0))
record.setRawHorizontalOverscanBBox(rawSerialOverscanBBox)
rawParallelOverscanBBox = self._makeBBoxFromList(
amp['rawParallelOverscanBBox'])
rawParallelOverscanBBox.shift(afwGeom.ExtentI(x0, y0))
record.setRawVerticalOverscanBBox(rawParallelOverscanBBox)
rawSerialPrescanBBox = self._makeBBoxFromList(
amp['rawSerialPrescanBBox'])
rawSerialPrescanBBox.shift(afwGeom.ExtentI(x0, y0))
record.setRawPrescanBBox(rawSerialPrescanBBox)
if perAmpData:
record.setRawXYOffset(
afwGeom.Extent2I(ix * xRawExtent, iy * yRawExtent))
else:
record.setRawXYOffset(afwGeom.Extent2I(0, 0))
record.setReadoutCorner(readCorners[amp['readCorner']])
record.setGain(amp['gain'])
record.setReadNoise(amp['readNoise'])
record.setSaturation(amp['saturation'])
record.setHasRawInfo(True)
# flip data when assembling if needs be (e.g. data from the serial at the top of a CCD)
flipX, flipY = amp.get("flipXY")
record.setRawFlipX(flipX)
record.setRawFlipY(flipY)
# linearity placeholder stuff
record.setLinearityCoeffs(
[float(val) for val in amp['linearityCoeffs']])
record.setLinearityType(amp['linearityType'])
record.set(linThreshKey, float(amp['linearityThreshold']))
record.set(linMaxKey, float(amp['linearityMax']))
record.set(linUnitsKey, "DN")
return ampCatalog
def makeMosaic(self,
images=None,
display="deferToFrame",
mode=None,
background=None,
title="",
frame=None):
"""Return a mosaic of all the images provided; if none are specified,
use the list accumulated with Mosaic.append().
Note that this mosaic is a patchwork of the input images; if you want to
make a mosaic of a set images of the sky, you probably want to use the coadd code
If display or frame (deprecated) is specified, display the mosaic
"""
if not images:
images = self.images
self.nImage = len(images)
if self.nImage == 0:
raise RuntimeError, "You must provide at least one image"
self.xsize, self.ysize = 0, 0
for im in images:
w, h = im.getWidth(), im.getHeight()
if w > self.xsize:
self.xsize = w
if h > self.ysize:
self.ysize = h
if background is None:
background = self.background
if mode is None:
mode = self.mode
if mode == "square":
nx, ny = 1, self.nImage
while nx * im.getWidth() < ny * im.getHeight():
nx += 1
ny = self.nImage // nx
if nx * ny < self.nImage:
ny += 1
if nx * ny < self.nImage:
nx += 1
if nx > self.nImage:
nx = self.nImage
assert (nx * ny >= self.nImage)
elif mode == "x":
nx, ny = self.nImage, 1
elif mode == "y":
nx, ny = 1, self.nImage
elif isinstance(mode, int):
nx = mode
ny = self.nImage // nx
if nx * ny < self.nImage:
ny += 1
else:
raise RuntimeError, ("Unknown mosaicing mode: %s" % mode)
self.nx, self.ny = nx, ny
mosaic = images[0].Factory(
afwGeom.Extent2I(nx * self.xsize + (nx - 1) * self.gutter,
ny * self.ysize + (ny - 1) * self.gutter))
try:
mosaic.set(self.background)
except AttributeError:
raise RuntimeError(
"Attempt to mosaic images of type %s which don't support set" %
type(mosaic))
for i in range(len(images)):
smosaic = mosaic.Factory(mosaic, self.getBBox(i % nx, i // nx),
afwImage.LOCAL)
im = images[i]
if smosaic.getDimensions() != im.getDimensions(
): # im is smaller than smosaic
llc = afwGeom.PointI(
(smosaic.getWidth() - im.getWidth()) // 2,
(smosaic.getHeight() - im.getHeight()) // 2)
smosaic = smosaic.Factory(
smosaic, afwGeom.Box2I(llc, im.getDimensions()),
afwImage.LOCAL)
smosaic <<= im
display = _getDisplayFromDisplayOrFrame(display, frame)
if display:
display.mtv(mosaic, title=title)
if images == self.images:
self.drawLabels(display=display)
return mosaic
def addAmp(ampCatalog, i, eparams):
""" Add an amplifier to an AmpInfoCatalog
@param ampCatalog: An instance of an AmpInfoCatalog object to fill with amp properties
@param i which amplifier? (i == 0 ? left : right)
@param eparams: Electronic parameters. This is a list of tuples with (i, params),
where params is a dictionary of electronic parameters.
"""
#
# Layout of active and overclock pixels in the as-readout data The layout is:
# Amp0 || extended | overclock | data || data | overclock | extended || Amp1
# for each row; all rows are identical in drift-scan data
#
height = 1361 # number of rows in a frame
width = 1024 # number of data pixels read out through one amplifier
nExtended = 8 # number of pixels in the extended register
nOverclock = 32 # number of (horizontal) overclock pixels
#
# Construct the needed bounding boxes given that geometrical information.
#
# Note that all the offsets are relative to the origin of this amp, not to its eventual
# position in the CCD
#
record = ampCatalog.addNew()
xtot = width + nExtended + nOverclock
allPixels = afwGeom.BoxI(afwGeom.PointI(0, 0),
afwGeom.ExtentI(xtot, height))
biasSec = afwGeom.BoxI(afwGeom.PointI(nExtended if i == 0 else width, 0),
afwGeom.ExtentI(nOverclock, height))
dataSec = afwGeom.BoxI(
afwGeom.PointI(nExtended + nOverclock if i == 0 else 0, 0),
afwGeom.ExtentI(width, height))
emptyBox = afwGeom.BoxI()
bbox = afwGeom.BoxI(afwGeom.PointI(0, 0), afwGeom.ExtentI(width, height))
bbox.shift(afwGeom.Extent2I(width * i, 0))
shiftp = afwGeom.Extent2I(xtot * i, 0)
allPixels.shift(shiftp)
biasSec.shift(shiftp)
dataSec.shift(shiftp)
record.setBBox(bbox)
record.setRawXYOffset(afwGeom.ExtentI(0, 0))
record.setName('left' if i == 0 else 'right')
record.setReadoutCorner(afwTable.LL if i == 0 else afwTable.LR)
record.setGain(eparams['gain'])
record.setReadNoise(eparams['readNoise'])
record.setSaturation(eparams['fullWell'])
record.setSuspectLevel(float("nan"))
record.setLinearityType(NullLinearityType)
record.setLinearityCoeffs([
1.,
])
record.setHasRawInfo(True)
record.setRawFlipX(False)
record.setRawFlipY(False)
record.setRawBBox(allPixels)
record.setRawDataBBox(dataSec)
record.setRawHorizontalOverscanBBox(biasSec)
record.setRawVerticalOverscanBBox(emptyBox)
record.setRawPrescanBBox(emptyBox)
def showPsfResiduals(exposure, sourceSet, magType="psf", scale=10, frame=None):
mimIn = exposure.getMaskedImage()
mimIn = mimIn.Factory(mimIn, True) # make a copy to subtract from
psf = exposure.getPsf()
psfWidth, psfHeight = psf.getLocalKernel().getDimensions()
#
# Make the image that we'll paste our residuals into. N.b. they can overlap the edges
#
w, h = int(mimIn.getWidth() / scale), int(mimIn.getHeight() / scale)
im = mimIn.Factory(w + psfWidth, h + psfHeight)
cenPos = []
for s in sourceSet:
x, y = s.getX(), s.getY()
sx, sy = int(x / scale + 0.5), int(y / scale + 0.5)
smim = im.Factory(
im,
afwGeom.BoxI(afwGeom.PointI(sx, sy),
afwGeom.ExtentI(psfWidth, psfHeight)))
sim = smim.getImage()
try:
if magType == "ap":
flux = s.getApFlux()
elif magType == "model":
flux = s.getModelFlux()
elif magType == "psf":
flux = s.getPsfFlux()
else:
raise RuntimeError("Unknown flux type %s" % magType)
subtractPsf(psf, mimIn, x, y, flux)
except Exception as e:
print(e)
try:
expIm = mimIn.getImage().Factory(
mimIn.getImage(),
afwGeom.BoxI(
afwGeom.PointI(
int(x) - psfWidth // 2,
int(y) - psfHeight // 2),
afwGeom.ExtentI(psfWidth, psfHeight)),
)
except pexExcept.Exception:
continue
cenPos.append([x - expIm.getX0() + sx, y - expIm.getY0() + sy])
sim += expIm
if frame is not None:
ds9.mtv(im, frame=frame)
with ds9.Buffering():
for x, y in cenPos:
ds9.dot("+", x, y, frame=frame)
return im
def write(self, dirName=".", fileName=None, metadata=None):
if not pyfits:
raise RuntimeError(
"I failed to import pyfits, so cannot write to disk")
if fileName is None:
fileName = self.fileNameFormat % (self.obsDate, self.arm,
self.spectrograph)
fullFileName = os.path.join(dirName, fileName)
#
# We'll pack all the traces into a single masked image, so figure out how large it needs to be
#
# Start by unpacking the traces' BBoxes; we need to do this anyway for the fits I/O
#
minX = []
minY = []
maxX = []
maxY = []
width = 0
for i in range(len(self.traces)):
bbox = self.traces[i].getBBox()
minX.append(bbox.getMinX())
minY.append(bbox.getMinY())
maxX.append(bbox.getMaxX())
maxY.append(bbox.getMaxY())
width += bbox.getWidth()
height = max(maxY) + 1
allTracesMI = afwImage.MaskedImageF(width, height)
# Copy trace's MaskedImages to allTracesMI
x0 = 0
origin = afwGeom.PointI(0, 0)
for i in range(len(self.traces)):
trace = self.traces[i]
xy0 = afwGeom.Point2I(x0, minY[i]) # origin in allTracesMI
allTracesMI[afwGeom.BoxI(xy0, trace.getDimensions())] = \
trace.Factory(trace, afwGeom.BoxI(origin, trace.getDimensions()), afwImage.LOCAL)
x0 += trace.getWidth()
#
# Time to actually write the data
#
if metadata is None:
hdr = dafBase.PropertySet()
else:
hdr = metadata
hdr.add('OBSTYPE', 'FIBERTRACE')
# Write fits file from MaskedImage
allTracesMI.writeFits(fullFileName, hdr)
# append the additional HDUs
hdu = pyfits.BinTableHDU.from_columns([
pyfits.Column(name='FIBERID',
format='J',
array=np.array(self.fiberId, dtype=np.int32)),
pyfits.Column(name='MINX',
format='J',
array=np.array(minX, dtype=np.int32)),
pyfits.Column(name='MINY',
format='J',
array=np.array(minY, dtype=np.int32)),
pyfits.Column(name='MAXX',
format='J',
array=np.array(maxX, dtype=np.int32)),
pyfits.Column(name='MAXY',
format='J',
array=np.array(maxY, dtype=np.int32)),
])
hdu.name = "ID_BOX"
hdu.header["INHERIT"] = True
# clobber=True in writeto prints a message, so use open instead
with pyfits.open(fullFileName, "update") as fd:
fd[1].name = "IMAGE"
fd[2].name = "MASK"
fd[3].name = "VARIANCE"
fd.append(hdu)
def _get_bbox(self, tract):
"""returns a afwGeom.BoxI instance given a `tract` and the current options (ra, dec, side_pixel)"""
xy = afwGeom.PointI(tract.getWcs().skyToPixel(self.radec))
cutout_size = afwGeom.ExtentI(self.side_pixel, self.side_pixel)
return afwGeom.BoxI(xy - cutout_size // 2, cutout_size)
def measure(self):
"""Detect and measure sources"""
mi = self.exposure.getMaskedImage()
#
# We do a pretty good job of interpolating, so don't propagagate the convolved CR/INTRP bits
# (we'll keep them for the original CR/INTRP pixels)
#
savedMask = mi.getMask().Factory(mi.getMask(), True)
saveBits = savedMask.getPlaneBitMask("CR") | \
savedMask.getPlaneBitMask("BAD") | \
savedMask.getPlaneBitMask("INTRP") # Bits to not convolve
savedMask &= saveBits
msk = mi.getMask(); msk &= ~saveBits; del msk # Clear the saved bits
#
# Smooth image
#
cnvImage = mi.Factory(mi.getBBox(afwImage.PARENT))
afwMath.convolve(cnvImage, mi, self.psf.getKernel(), afwMath.ConvolutionControl())
msk = cnvImage.getMask(); msk |= savedMask; del msk # restore the saved bits
threshold = afwDetection.Threshold(3, afwDetection.Threshold.STDEV)
#
# Only search the part of the frame that was PSF-smoothed
#
llc = afwGeom.PointI(self.psf.getKernel().getWidth()/2, self.psf.getKernel().getHeight()/2)
urc = afwGeom.PointI(cnvImage.getWidth() - 1, cnvImage.getHeight() - 1) - afwGeom.ExtentI(llc[0], llc[1]);
middle = cnvImage.Factory(cnvImage, afwGeom.BoxI(llc, urc), afwImage.LOCAL)
ds = afwDetection.FootprintSetF(middle, threshold, "DETECTED")
del middle
#
# ds only searched the middle but it belongs to the entire MaskedImage
#
ds.setRegion(mi.getBBox(afwImage.PARENT))
#
# We want to grow the detections into the edge by at least one pixel so that it sees the EDGE bit
#
grow, isotropic = 1, False
ds = afwDetection.FootprintSetF(ds, grow, isotropic)
ds.setMask(mi.getMask(), "DETECTED")
#
# Reinstate the saved (e.g. BAD) (and also the DETECTED | EDGE) bits in the unsmoothed image
#
savedMask <<= cnvImage.getMask()
msk = mi.getMask(); msk |= savedMask; del msk
del savedMask; savedMask = None
#msk = mi.getMask(); msk &= ~0x10; del msk # XXXX
if self.display:
ds9.mtv(mi, frame = 0, lowOrderBits = True)
ds9.mtv(cnvImage, frame = 1)
objects = ds.getFootprints()
#
# Time to actually measure
#
msPolicy = policy.Policy.createPolicy(policy.DefaultPolicyFile("meas_algorithms",
"examples/measureSources.paf"))
msPolicy = msPolicy.getPolicy("measureSources")
measureSources = measAlg.makeMeasureSources(self.exposure, msPolicy)
self.sourceList = afwDetection.SourceSet()
for i in range(len(objects)):
source = afwDetection.Source()
self.sourceList.append(source)
source.setId(i)
source.setFlagForDetection(source.getFlagForDetection() | measAlg.Flags.BINNED1);
try:
measureSources.apply(source, objects[i])
except Exception, e:
try:
print e
except Exception, ee:
print ee
def makeRaft(raftName):
dewar = cameraGeom.Raft(cameraGeom.Id("DECam"), 1, 1)
dewar.addDetector(afwGeom.PointI(0, 0), cameraGeom.FpPoint(0.0, 0.0),
cameraGeom.Orientation(0), makeCcd(raftName))
return dewar
def setBrightObjectMasks(self, exposure, dataId, brightObjectMasks):
"""Set the bright object masks
exposure: Exposure under consideration
dataId: Data identifier dict for patch
brightObjectMasks: afwTable of bright objects to mask
# ---------------------------------------------- #
copied from AssembleCoaddTask. To be implemented so that it
can be imported more easily
see https://jira.lsstcorp.org/browse/DM-15030
# ---------------------------------------------- #
"""
#
# Check the metadata specifying the tract/patch/filter
#
if brightObjectMasks is None:
self.log.warn("Unable to apply bright object mask: none supplied")
return
self.log.info("Applying %d bright object masks to %s",
len(brightObjectMasks), dataId)
md = brightObjectMasks.table.getMetadata()
for k in dataId:
if not md.exists(k):
self.log.warn("Expected to see %s in metadata", k)
else:
if md.get(k) != dataId[k]:
self.log.warn(
"Expected to see %s == %s in metadata, saw %s", k,
md.get(k), dataId[k])
mask = exposure.getMaskedImage().getMask()
wcs = exposure.getWcs()
plateScale = wcs.getPixelScale().asArcseconds()
for rec in brightObjectMasks:
center = afwGeom.PointI(wcs.skyToPixel(rec.getCoord()))
if rec["type"] == "box":
assert rec["angle"] == 0.0, ("Angle != 0 for mask object %s" %
rec["id"])
width = rec["width"].asArcseconds(
) / plateScale # convert to pixels
height = rec["height"].asArcseconds(
) / plateScale # convert to pixels
halfSize = afwGeom.ExtentI(0.5 * width, 0.5 * height)
bbox = afwGeom.Box2I(center - halfSize, center + halfSize)
bbox = afwGeom.BoxI(
afwGeom.PointI(int(center[0] - 0.5 * width),
int(center[1] - 0.5 * height)),
afwGeom.PointI(int(center[0] + 0.5 * width),
int(center[1] + 0.5 * height)))
spans = afwGeom.SpanSet(bbox)
elif rec["type"] == "circle":
radius = int(rec["radius"].asArcseconds() /
plateScale) # convert to pixels
spans = afwGeom.SpanSet.fromShape(radius, offset=center)
else:
self.log.warn("Unexpected region type %s at %s" % rec["type"],
center)
continue
spans.clippedTo(mask.getBBox()).setMask(mask,
self.brightObjectBitmask)
def readFits(fileName, hdu=0, flags=0):
"""Read a our list of Backgrounds from a file
@param fileName FITS file to read
@param hdu First Header/Data Unit to attempt to read from
@param flags Flags to control details of reading; currently unused,
but present for consistency with
afw.table.BaseCatalog.readFits.
See also getImage()
"""
if not isinstance(fileName,
MemFileManager) and not os.path.exists(fileName):
raise RuntimeError("File not found: %s" % fileName)
self = BackgroundList()
while True:
hdu += 1
md = dafBase.PropertyList()
try:
img = afwImage.ImageF(fileName, hdu, md)
hdu += 1
except FitsError:
break
msk = afwImage.MaskU(fileName, hdu)
hdu += 1
var = afwImage.ImageF(fileName, hdu)
statsImage = afwImage.makeMaskedImage(img, msk, var)
x0 = md.get("BKGD_X0")
y0 = md.get("BKGD_Y0")
width = md.get("BKGD_WIDTH")
height = md.get("BKGD_HEIGHT")
imageBBox = afwGeom.BoxI(afwGeom.PointI(x0, y0),
afwGeom.ExtentI(width, height))
interpStyle = md.get("INTERPSTYLE")
undersampleStyle = md.get("UNDERSAMPLESTYLE")
# Older outputs won't have APPROX* settings. Provide alternative defaults.
# Note: Currently X- and Y-orders must be equal due to a limitation in
# math::Chebyshev1Function2. Setting approxOrderY = -1 is equivalent
# to saying approxOrderY = approxOrderX.
approxStyle = md.get("APPROXSTYLE") if "APPROXSTYLE" in md.names() \
else afwMath.ApproximateControl.UNKNOWN
approxOrderX = md.get(
"APPROXORDERX") if "APPROXORDERX" in md.names() else 1
approxOrderY = md.get(
"APPROXORDERY") if "APPROXORDERY" in md.names() else -1
approxWeighting = md.get(
"APPROXWEIGHTING") if "APPROXWEIGHTING" in md.names() else True
bkgd = afwMath.BackgroundMI(imageBBox, statsImage)
bctrl = bkgd.getBackgroundControl()
bctrl.setInterpStyle(interpStyle)
bctrl.setUndersampleStyle(undersampleStyle)
actrl = afwMath.ApproximateControl(approxStyle, approxOrderX,
approxOrderY, approxWeighting)
bctrl.setApproximateControl(actrl)
bgInfo = (bkgd, interpStyle, undersampleStyle, approxStyle,
approxOrderX, approxOrderY, approxWeighting)
self.append(bgInfo)
return self
# From SimonK's tutorial
# 3/31/2017 -- MSSG
import sys
import lsst.afw.image as afwImage
import lsst.afw.geom as afwGeom
from lsst.pex.exceptions import LengthError
import lsst.afw.math as afwMath
import lsst.afw.detection as afwDetect
import lsst.afw.display as afwDisplay
n_objects = 1000
box = afwGeom.BoxI(afwGeom.PointI(300, 500), afwGeom.ExtentI(2000, 2048))
im = afwImage.ImageF(box)
print("im.getXY0() = ", im.getXY0())
subbox = afwGeom.BoxI(afwGeom.PointI(10, 10), afwGeom.ExtentI(100, 100))
try:
im2 = afwImage.ImageF(im, subbox)
except LengthError:
im2 = afwImage.ImageF(im, subbox, afwImage.LOCAL)
print("im2.getXY0() = ", im2.getXY0())
rand = afwMath.Random()
buffer_xy = 150 # Don't put objects near the edges
x_positions = [
def makeBbox(x0, y0, x_extent, y_extent):
return afwGeom.BoxI(afwGeom.PointI(x0, y0),
afwGeom.ExtentI(x_extent, y_extent))
def testDetection(self):
"""Test object detection"""
#
# Fix defects
#
# Mask known bad pixels
#
measAlgorithmsDir = lsst.utils.getPackageDir('meas_algorithms')
badPixels = defects.policyToBadRegionList(
os.path.join(measAlgorithmsDir, "policy/BadPixels.paf"))
# did someone lie about the origin of the maskedImage? If so, adjust bad pixel list
if self.XY0.getX() != self.mi.getX0() or self.XY0.getY(
) != self.mi.getY0():
dx = self.XY0.getX() - self.mi.getX0()
dy = self.XY0.getY() - self.mi.getY0()
for bp in badPixels:
bp.shift(-dx, -dy)
algorithms.interpolateOverDefects(self.mi, self.psf, badPixels)
#
# Subtract background
#
bgGridSize = 64 # was 256 ... but that gives only one region and the spline breaks
bctrl = afwMath.BackgroundControl(afwMath.Interpolate.NATURAL_SPLINE)
bctrl.setNxSample(int(self.mi.getWidth() / bgGridSize) + 1)
bctrl.setNySample(int(self.mi.getHeight() / bgGridSize) + 1)
backobj = afwMath.makeBackground(self.mi.getImage(), bctrl)
self.mi.getImage()[:] -= backobj.getImageF()
#
# Remove CRs
#
crConfig = algorithms.FindCosmicRaysConfig()
crs = algorithms.findCosmicRays(self.mi, self.psf, 0,
pexConfig.makePolicy(crConfig))
#
# We do a pretty good job of interpolating, so don't propagagate the convolved CR/INTRP bits
# (we'll keep them for the original CR/INTRP pixels)
#
savedMask = self.mi.getMask().Factory(self.mi.getMask(), True)
saveBits = savedMask.getPlaneBitMask("CR") | \
savedMask.getPlaneBitMask("BAD") | \
savedMask.getPlaneBitMask("INTRP") # Bits to not convolve
savedMask &= saveBits
msk = self.mi.getMask()
msk &= ~saveBits # Clear the saved bits
del msk
#
# Smooth image
#
psf = algorithms.DoubleGaussianPsf(
15, 15, self.FWHM / (2 * math.sqrt(2 * math.log(2))))
cnvImage = self.mi.Factory(self.mi.getBBox())
kernel = psf.getKernel()
afwMath.convolve(cnvImage, self.mi, kernel,
afwMath.ConvolutionControl())
msk = cnvImage.getMask()
msk |= savedMask # restore the saved bits
del msk
threshold = afwDetection.Threshold(3, afwDetection.Threshold.STDEV)
#
# Only search the part of the frame that was PSF-smoothed
#
llc = afwGeom.PointI(psf.getKernel().getWidth() / 2,
psf.getKernel().getHeight() / 2)
urc = afwGeom.PointI(cnvImage.getWidth() - llc[0] - 1,
cnvImage.getHeight() - llc[1] - 1)
middle = cnvImage.Factory(cnvImage, afwGeom.BoxI(llc, urc),
afwImage.LOCAL)
ds = afwDetection.FootprintSet(middle, threshold, "DETECTED")
del middle
#
# Reinstate the saved (e.g. BAD) (and also the DETECTED | EDGE) bits in the unsmoothed image
#
savedMask[:] = cnvImage.getMask()
msk = self.mi.getMask()
msk |= savedMask
del msk
del savedMask
if display:
ds9.mtv(self.mi, frame=0)
ds9.mtv(cnvImage, frame=1)
#
# Time to actually measure
#
schema = afwTable.SourceTable.makeMinimalSchema()
sfm_config = measBase.SingleFrameMeasurementConfig()
sfm_config.plugins = [
"base_SdssCentroid", "base_CircularApertureFlux", "base_PsfFlux",
"base_SdssShape", "base_GaussianFlux",
"base_ClassificationExtendedness", "base_PixelFlags"
]
sfm_config.slots.centroid = "base_SdssCentroid"
sfm_config.slots.shape = "base_SdssShape"
sfm_config.slots.psfFlux = "base_PsfFlux"
sfm_config.slots.instFlux = None
sfm_config.slots.apFlux = "base_CircularApertureFlux_3_0"
sfm_config.slots.modelFlux = "base_GaussianFlux"
sfm_config.slots.calibFlux = None
sfm_config.plugins["base_SdssShape"].maxShift = 10.0
sfm_config.plugins["base_CircularApertureFlux"].radii = [3.0]
task = measBase.SingleFrameMeasurementTask(schema, config=sfm_config)
measCat = afwTable.SourceCatalog(schema)
# detect the sources and run with the measurement task
ds.makeSources(measCat)
self.exposure.setPsf(self.psf)
task.run(measCat, self.exposure)
for source in measCat:
if source.get("base_PixelFlags_flag_edge"):
continue
if display:
ds9.dot("+",
source.getX() - self.mi.getX0(),
source.getY() - self.mi.getY0())
def testEdge(self):
"""Test that we can interpolate to the edge"""
mi = afwImage.MaskedImageF(80, 30)
ima = mi.getImage().getArray()
#
# Loop over number of bad columns at left or right edge of image
#
for nBadCol in range(0, 20):
mi.set((0, 0x0, 0))
np.random.seed(666)
ima[:] = np.random.uniform(-1, 1, ima.shape)
defects = []
if nBadCol > 0:
#
# Bad left edge
#
ima[:, 0:nBadCol] = 10
defects.append(
afwGeom.BoxI(afwGeom.PointI(0, 0),
afwGeom.ExtentI(nBadCol, mi.getHeight())))
#
# With another bad set of columns next to bad left edge
#
ima[:, -nBadCol:] = 10
defects.append(
afwGeom.BoxI(afwGeom.PointI(mi.getWidth() - nBadCol, 0),
afwGeom.ExtentI(nBadCol, mi.getHeight())))
#
# Bad right edge
#
ima[0:10, nBadCol + 1:nBadCol + 4] = 100
defects.append(
afwGeom.BoxI(afwGeom.PointI(nBadCol + 1, 0),
afwGeom.ExtentI(3, 10)))
#
# With another bad set of columns next to bad right edge
#
ima[0:10, -nBadCol - 4:-nBadCol - 1] = 100
defects.append((afwGeom.BoxI(
afwGeom.PointI(mi.getWidth() - nBadCol - 4, 0),
afwGeom.ExtentI(3, 10))))
#
# Test cases that left and right bad patches nearly (or do) coalesce
#
ima[-3:, 0:mi.getWidth() // 2 - 1] = 100
defects.append(
afwGeom.BoxI(afwGeom.PointI(0,
mi.getHeight() - 3),
afwGeom.ExtentI(mi.getWidth() // 2 - 1, 1)))
ima[-3:, mi.getWidth() // 2 + 1:] = 100
defects.append(
afwGeom.BoxI(
afwGeom.PointI(mi.getWidth() // 2 + 1,
mi.getHeight() - 3),
afwGeom.ExtentI(mi.getWidth() // 2 - 1, 1)))
ima[-2:, 0:mi.getWidth() // 2] = 100
defects.append(
afwGeom.BoxI(afwGeom.PointI(0,
mi.getHeight() - 2),
afwGeom.ExtentI(mi.getWidth() // 2, 1)))
ima[-2:, mi.getWidth() // 2 + 1:] = 100
defects.append(
afwGeom.BoxI(
afwGeom.PointI(mi.getWidth() // 2 + 1,
mi.getHeight() - 2),
afwGeom.ExtentI(mi.getWidth() // 2 - 1, 1)))
ima[-1:, :] = 100
defects.append(
afwGeom.BoxI(afwGeom.PointI(0,
mi.getHeight() - 1),
afwGeom.ExtentI(mi.getWidth(), 1)))
# Test fix for HSC-978: long defect stops one pixel shy of the edge (when nBadCol == 0)
ima[13, :-1] = 100
defects.append(
afwGeom.BoxI(afwGeom.PointI(0, 13),
afwGeom.ExtentI(mi.getWidth() - 1, 1)))
ima[14, 1:] = 100
defects.append(
afwGeom.BoxI(afwGeom.PointI(1, 14),
afwGeom.ExtentI(mi.getWidth() - 1, 1)))
#
# Build list of defects to interpolate over
#
defectList = []
for bbox in defects:
defectList.append(algorithms.Defect(bbox))
#
# Guess a PSF and do the work
#
if display:
ds9.mtv(mi, frame=0)
psf = algorithms.DoubleGaussianPsf(
15, 15, 1. / (2 * math.sqrt(2 * math.log(2))))
algorithms.interpolateOverDefects(mi, psf, defectList, 0, True)
if display:
ds9.mtv(mi, frame=1)
self.assertGreater(np.min(ima), -2)
self.assertGreater(2, np.max(ima))
def _makeBBoxFromList(ylist):
"""Given a list [(x0, y0), (xsize, ysize)], probably from a yaml file, return a BoxI
"""
(x0, y0), (xsize, ysize) = ylist
return afwGeom.BoxI(afwGeom.PointI(x0, y0), afwGeom.ExtentI(xsize, ysize))
def showCamera(camera,
imageSource=FakeImageDataSource(),
imageFactory=afwImage.ImageF,
detectorNameList=None,
binSize=10,
bufferSize=10,
frame=None,
overlay=True,
title="",
ctype=afwDisplay.GREEN,
textSize=1.25,
originAtCenter=True,
display=None,
**kwargs):
"""!Show a Camera on display, with the specified display
The rotation of the sensors is snapped to the nearest multiple of 90 deg.
Also note that the pixel size is constant over the image array. The lower left corner (LLC) of each
sensor amp is snapped to the LLC of the pixel containing the LLC of the image.
if overlay show the IDs and detector boundaries
@param[in] camera Camera to show
@param[in] imageSource Source to get Ccd images from. Must have a getCcdImage method.
@param[in] imageFactory Type of image to make
@param[in] detectorNameList List of names of Detectors to use. If None use all
@param[in] binSize bin factor
@param[in] bufferSize size of border in binned pixels to make around camera image.
@param[in] frame specify image display (@deprecated; new code should use display)
@param[in] overlay Overlay Detector IDs and boundaries?
@param[in] title Title in display
@param[in] ctype Color to use when drawing Detector boundaries
@param[in] textSize Size of detector labels
@param[in] originAtCenter Put origin of the camera WCS at the center of the image? Else it will be LL
@param[in] display image display on which to display
@param[in] **kwargs all remaining keyword arguments are passed to makeImageFromCamera
@return the mosaic image
"""
display = _getDisplayFromDisplayOrFrame(display, frame)
if binSize < 1:
binSize = 1
cameraImage = makeImageFromCamera(camera,
detectorNameList=detectorNameList,
bufferSize=bufferSize,
imageSource=imageSource,
imageFactory=imageFactory,
binSize=binSize,
**kwargs)
if detectorNameList is None:
ccdList = [camera[name] for name in camera.getNameIter()]
else:
ccdList = [camera[name] for name in detectorNameList]
if detectorNameList is None:
camBbox = camera.getFpBBox()
else:
camBbox = afwGeom.Box2D()
for detName in detectorNameList:
for corner in camera[detName].getCorners(FOCAL_PLANE):
camBbox.include(corner)
pixelSize = ccdList[0].getPixelSize()
if originAtCenter:
#Can't divide SWIGGED extent type things when division is imported
#from future. This is DM-83
ext = cameraImage.getBBox().getDimensions()
wcsReferencePixel = afwGeom.PointI(ext.getX() // 2, ext.getY() // 2)
else:
wcsReferencePixel = afwGeom.Point2I(0, 0)
wcs = makeFocalPlaneWcs(pixelSize * binSize, wcsReferencePixel)
if display:
if title == "":
title = camera.getName()
display.mtv(cameraImage, title=title, wcs=wcs)
if overlay:
with display.Buffering():
camBbox = getCameraImageBBox(camBbox, pixelSize,
bufferSize * binSize)
bboxList = getCcdInCamBBoxList(ccdList, binSize, pixelSize,
camBbox.getMin())
for bbox, ccd in zip(bboxList, ccdList):
nQuarter = ccd.getOrientation().getNQuarter()
# borderWidth to 0.5 to align with the outside edge of the pixel
displayUtils.drawBBox(bbox,
borderWidth=0.5,
ctype=ctype,
display=display)
dims = bbox.getDimensions()
display.dot(ccd.getName(),
bbox.getMinX() + dims.getX() / 2,
bbox.getMinY() + dims.getY() / 2,
ctype=ctype,
size=textSize,
textAngle=nQuarter * 90)
return cameraImage
jmp = {}
jmp['x'] = []
jmp['y'] = []
jmp['flux'] = []
for line in file:
if line.startswith('#'):
continue
x = float(line.split()[0])
y = float(line.split()[1])
if ccdnum < 18:
x = imsize.getX() - x + xoff
y = imsize.getY() - y + yoff
else:
x = x - xoff
y = y - yoff
pt = afwGeom.PointI(int(x), int(y))
if bbox.contains(pt):
jmp['x'].append(x)
jmp['y'].append(y)
jmp['flux'].append(float(line.split()[2]))
jmp['x'] = np.array(jmp['x'], float)
jmp['y'] = np.array(jmp['y'], float)
jmp['flux'] = np.array(jmp['flux'], float)
except IOError:
print 'No JMP file %s' %(jmpfile)
jmp = {}
jmp['x'] = []
jmp['y'] = []
jmp['flux'] = []
try:
def make_postage_stamp_plot(self,
index,
stamp_size=100,
reverse_colormap=False):
"""
Gather the data for creating the source postage stamp plot.
Parameters
----------
index : int
The location of the source data in the Source table.
stamp_size : int, optional
Size of the generated postage stamp in pixels.
reverse_colormap : bool, optional
Reverse the colormap. Default is black (low) to white (high).
"""
ra_target, dec_target = (
self.src['coord_ra'][self.good_indexes][index],
self.src['coord_dec'][self.good_indexes][index]) # Radians
radec = afwGeom.SpherePoint(ra_target, dec_target, afwGeom.radians)
cutoutSize = afwGeom.ExtentI(stamp_size, stamp_size)
wcs = self.calexp.getWcs()
# Note: This call fails in version 15.0. Requires a weekly after that release.
xy = afwGeom.PointI(wcs.skyToPixel(radec))
bbox = afwGeom.BoxI(xy - cutoutSize // 2, cutoutSize)
# Check for bounds that fall off the edges of the image. Need to clip them to the
# image boundary otherwise the calexp_sub call fails.
calexp_extent = self.calexp.getDimensions()
clipped_bbox = afwGeom.BoxI(afwGeom.PointI(0, 0), calexp_extent)
clipped_bbox.clip(bbox)
# Postage stamp image only
cutout_image = self.butler.get('calexp_sub',
bbox=clipped_bbox,
immediate=True,
dataId=self.dataid).getMaskedImage()
vmin, vmax = self.zscale.get_limits(cutout_image.image.array)
self.image_src.data = {
'img': [cutout_image.image.array],
'x': [0],
'y': [0],
'dw': [clipped_bbox.getDimensions().getX()],
'dh': [clipped_bbox.getDimensions().getY()]
}
gc = gray(256)
if reverse_colormap:
gc.reverse()
lcm = LinearColorMapper(palette=gc, low=vmin, high=vmax)
self.img_plt.image('img',
'x',
'y',
'dw',
'dh',
color_mapper=lcm,
source=self.image_src)
# Color bar doesn't come up properly. Need to work on this later.
colorbar = ColorBar(color_mapper=lcm,
ticker=BasicTicker(),
border_line_color=None,
label_standoff=5,
location=(0, 0))
# self.img_plt.add_layout(colorbar, 'right')
# Does the cutout_image have a wcs? It does not appear to...
self.img_plt.circle(xy.getX() - cutout_image.getX0(),
xy.getY() - cutout_image.getY0(),
fill_color=None,
line_color='red',
radius=int(0.05 * stamp_size))
def addAmp(ampCatalog, amp, eparams):
""" Add an amplifier to an AmpInfoCatalog
@param ampCatalog: An instance of an AmpInfoCatalog object to fill with amp properties
@param amp: Dictionary of amp geometric properties
@param eparams: Dictionary of amp electronic properties for this amp
"""
record = ampCatalog.addNew()
xtot = amp['ewidth']
ytot = amp['eheight']
allPixels = afwGeom.Box2I(afwGeom.Point2I(0, 0), afwGeom.Extent2I(xtot, ytot))
biassl = amp['biassec']
biasSec = afwGeom.Box2I(afwGeom.Point2I(biassl[0], biassl[1]), afwGeom.Point2I(biassl[2], biassl[3]))
datasl = amp['datasec']
dataSec = afwGeom.Box2I(afwGeom.Point2I(datasl[0], datasl[1]), afwGeom.Point2I(datasl[2], datasl[3]))
extended = dataSec.getMin().getX()
voverscan = ytot - dataSec.getMaxY()
pscan = dataSec.getMin().getY()
if not voverscan:
voscanSec = afwGeom.Box2I(afwGeom.Point2I(extended, dataSec.getMax().getY()),
afwGeom.Extent2I(dataSec.getDimensions().getX(), 0))
else:
voscanSec = afwGeom.Box2I(afwGeom.Point2I(extended, dataSec.getMax().getY()+1),
afwGeom.Extent2I(dataSec.getDimensions().getX(), voverscan))
pscanSec = afwGeom.Box2I(afwGeom.Point2I(extended, 0),
afwGeom.Extent2I(dataSec.getDimensions().getX(), pscan))
if amp['flipX']:
#No need to flip bbox or allPixels since they
#are at the origin and span the full pixel grid
biasSec.flipLR(xtot)
dataSec.flipLR(xtot)
voscanSec.flipLR(xtot)
pscanSec.flipLR(xtot)
bbox = afwGeom.BoxI(afwGeom.PointI(0, 0), dataSec.getDimensions())
bbox.shift(afwGeom.Extent2I(dataSec.getDimensions().getX()*eparams['index'][0], 0))
shiftp = afwGeom.Extent2I(xtot*eparams['index'][0], 0)
allPixels.shift(shiftp)
biasSec.shift(shiftp)
dataSec.shift(shiftp)
voscanSec.shift(shiftp)
pscanSec.shift(shiftp)
record.setBBox(bbox)
record.setRawXYOffset(afwGeom.ExtentI(0,0))
#Set amplifier names according to the CFHT convention (A, B)
if eparams['index'][0] == 0 and eparams['index'][1] == 0 :
record.setName("A")
elif eparams['index'][0] == 1 and eparams['index'][1] == 0 :
record.setName("B")
else :
raise ValueError("Unexpected index parameter %i, %i"%(eparams['index'][0], eparams['index'][1]))
record.setReadoutCorner(afwTable.LR if amp['flipX'] else afwTable.LL)
record.setGain(eparams['gain'])
record.setReadNoise(eparams['readNoise'])
record.setSaturation(int(eparams['saturation']))
#The files do not have any linearity information
record.setLinearityType('Proportional')
record.setLinearityCoeffs([1.,])
record.setHasRawInfo(True)
record.setRawFlipX(False)
record.setRawFlipY(False)
record.setRawBBox(allPixels)
record.setRawDataBBox(dataSec)
record.setRawHorizontalOverscanBBox(biasSec)
record.setRawVerticalOverscanBBox(voscanSec)
record.setRawPrescanBBox(pscanSec)
def fitPatches(exp,
nx=4,
ny=8,
bin=None,
frame=None,
returnResidualImage=False,
r=None,
lnGrad=None,
theta=None):
"""Fit planes to a set of patches of an image im
If r, theta, and lnGrad are provided they should be lists (more accurately, support .append), and they have the
values of r, theta, and dlnI/dr from this image appended.
"""
width, height = exp.getDimensions()
if bin is not None:
nx, ny = width // bin, height // bin
xsize = width // nx
ysize = height // ny
if frame is not None:
frame0 = frame
ds9.mtv(exp, title="im", frame=frame) if True else None
frame += 1
if hasattr(exp, "getMaskedImage"):
mi = exp.getMaskedImage()
ccd = exp.getDetector()
else:
mi = exp
ccd = None
try:
mi = mi.convertF()
except AttributeError:
pass
if r is not None or lnGrad is not None:
assert ccd is not None, "I need a CCD to set r and the logarithmic gradient"
assert r is not None and lnGrad is not None, "Please provide both r and lnGrad"
z = afwImage.ImageF(nx, ny)
za = z.getArray()
dlnzdx = afwImage.ImageF(nx, ny)
dlnzdxa = dlnzdx.getArray()
dlnzdy = afwImage.ImageF(nx, ny)
dlnzdya = dlnzdy.getArray()
dlnzdr = afwImage.ImageF(nx, ny)
dlnzdra = dlnzdr.getArray()
if returnResidualImage:
residualImage = mi.clone()
try:
residualImage = residualImage.convertF()
except AttributeError:
pass
else:
residualImage = None
with ds9.Buffering():
for iy in range(ny):
for ix in range(nx):
bbox = afwGeom.BoxI(afwGeom.PointI(ix * xsize, iy * ysize),
afwGeom.ExtentI(xsize, ysize))
if False and frame is not None:
ds9Utils.drawBBox(bbox, frame=frame0)
b, res = fitPlane(mi[bbox].getImage(),
returnResidualImage=returnResidualImage,
niter=5)
b[1:] /= b[0]
za[iy, ix], dlnzdxa[iy, ix], dlnzdya[iy, ix] = b
if returnResidualImage:
residualImage[bbox][:] = res
if ccd:
cen = afwGeom.PointD(bbox.getBegin() +
bbox.getDimensions() / 2)
x, y = ccd.getPositionFromPixel(
cen).getMm() # I really want pixels here
t = np.arctan2(y, x)
dlnzdra[iy, ix] = np.cos(t) * dlnzdxa[iy, ix] + np.sin(
t) * dlnzdya[iy, ix]
if r is not None:
if not (ix in (0, xsize - 1) or iy in (0, ysize - 1)):
r.append(np.hypot(x, y))
lnGrad.append(dlnzdra[iy, ix])
if theta is not None:
theta.append(t)
if frame is not None:
if False:
ds9.mtv(z, title="z", frame=frame)
frame += 1
ds9.mtv(dlnzdx, title="dlnz/dx", frame=frame)
frame += 1
ds9.mtv(dlnzdy, title="dlnz/dy", frame=frame)
frame += 1
ds9.mtv(residualImage, title="res", frame=frame)
frame += 1
ds9.mtv(dlnzdr,
title="dlnz/dr %s" % (ccd.getId().getSerial() if ccd else ""),
frame=frame)
frame += 1
return dlnzdx, dlnzdy, dlnzdr, residualImage
def setUp(self):
width, height = 100, 300
self.mi = afwImage.MaskedImageF(afwGeom.ExtentI(width, height))
self.mi.set(0)
self.mi.getVariance().set(10)
self.mi.getMask().addMaskPlane("DETECTED")
self.FWHM = 5
self.ksize = 25 # size of desired kernel
self.exposure = afwImage.makeExposure(self.mi)
psf = roundTripPsf(
2,
algorithms.DoubleGaussianPsf(self.ksize, self.ksize,
self.FWHM / (2 * sqrt(2 * log(2))), 1,
0.1))
self.exposure.setPsf(psf)
for x, y in [
(20, 20),
#(30, 35), (50, 50),
(60, 20),
(60, 210),
(20, 210)
]:
flux = 10000 - 0 * x - 10 * y
sigma = 3 + 0.01 * (y - self.mi.getHeight() / 2)
psf = roundTripPsf(
3,
algorithms.DoubleGaussianPsf(self.ksize, self.ksize, sigma, 1,
0.1))
im = psf.computeImage().convertF()
im *= flux
smi = self.mi.getImage().Factory(
self.mi.getImage(),
afwGeom.BoxI(
afwGeom.PointI(x - self.ksize / 2, y - self.ksize / 2),
afwGeom.ExtentI(self.ksize)), afwImage.LOCAL)
if False: # Test subtraction with non-centered psfs
im = afwMath.offsetImage(im, 0.5, 0.5)
smi += im
del psf
del im
del smi
psf = roundTripPsf(
4,
algorithms.DoubleGaussianPsf(self.ksize, self.ksize,
self.FWHM / (2 * sqrt(2 * log(2))), 1,
0.1))
self.cellSet = afwMath.SpatialCellSet(
afwGeom.BoxI(afwGeom.PointI(0, 0), afwGeom.ExtentI(width, height)),
100)
ds = afwDetection.FootprintSet(self.mi, afwDetection.Threshold(10),
"DETECTED")
#
# Prepare to measure
#
msConfig = algorithms.SourceMeasurementConfig()
msConfig.load("tests/config/MeasureSources.py")
schema = afwTable.SourceTable.makeMinimalSchema()
measureSources = msConfig.makeMeasureSources(schema)
catalog = afwTable.SourceCatalog(schema)
msConfig.slots.calibFlux = None
msConfig.slots.setupTable(catalog.table)
ds.makeSources(catalog)
for i, source in enumerate(catalog):
measureSources.applyWithPeak(source, self.exposure)
self.cellSet.insertCandidate(
algorithms.makePsfCandidate(source, self.exposure))
def showPsfCandidates(exposure, psfCellSet, psf=None, frame=None, normalize=True, showBadCandidates=True,
fitBasisComponents=False, variance=None, chi=None):
"""Display the PSF candidates.
If psf is provided include PSF model and residuals; if normalize is true normalize the PSFs
(and residuals)
If chi is True, generate a plot of residuals/sqrt(variance), i.e. chi
If fitBasisComponents is true, also find the best linear combination of the PSF's components
(if they exist)
"""
if chi is None:
if variance is not None: # old name for chi
chi = variance
#
# Show us the ccandidates
#
mos = displayUtils.Mosaic()
#
candidateCenters = []
candidateCentersBad = []
candidateIndex = 0
for cell in psfCellSet.getCellList():
for cand in cell.begin(False): # include bad candidates
rchi2 = cand.getChi2()
if rchi2 > 1e100:
rchi2 = numpy.nan
if not showBadCandidates and cand.isBad():
continue
if psf:
im_resid = displayUtils.Mosaic(gutter=0, background=-5, mode="x")
try:
im = cand.getMaskedImage() # copy of this object's image
xc, yc = cand.getXCenter(), cand.getYCenter()
margin = 0 if True else 5
w, h = im.getDimensions()
bbox = afwGeom.BoxI(afwGeom.PointI(margin, margin), im.getDimensions())
if margin > 0:
bim = im.Factory(w + 2*margin, h + 2*margin)
stdev = numpy.sqrt(afwMath.makeStatistics(im.getVariance(), afwMath.MEAN).getValue())
afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
bim.getVariance().set(stdev**2)
bim.assign(im, bbox)
im = bim
xc += margin
yc += margin
im = im.Factory(im, True)
im.setXY0(cand.getMaskedImage().getXY0())
except:
continue
if not variance:
im_resid.append(im.Factory(im, True))
if True: # tweak up centroids
mi = im
psfIm = mi.getImage()
config = measBase.SingleFrameMeasurementTask.ConfigClass()
config.slots.centroid = "base_SdssCentroid"
schema = afwTable.SourceTable.makeMinimalSchema()
measureSources = measBase.SingleFrameMeasurementTask(schema, config=config)
catalog = afwTable.SourceCatalog(schema)
extra = 10 # enough margin to run the sdss centroider
miBig = mi.Factory(im.getWidth() + 2*extra, im.getHeight() + 2*extra)
miBig[extra:-extra, extra:-extra] = mi
miBig.setXY0(mi.getX0() - extra, mi.getY0() - extra)
mi = miBig
del miBig
exp = afwImage.makeExposure(mi)
exp.setPsf(psf)
footprintSet = afwDet.FootprintSet(mi,
afwDet.Threshold(0.5*numpy.max(psfIm.getArray())),
"DETECTED")
footprintSet.makeSources(catalog)
if len(catalog) == 0:
raise RuntimeError("Failed to detect any objects")
measureSources.run(catalog, exp)
if len(catalog) == 1:
source = catalog[0]
else: # more than one source; find the once closest to (xc, yc)
dmin = None # an invalid value to catch logic errors
for i, s in enumerate(catalog):
d = numpy.hypot(xc - s.getX(), yc - s.getY())
if i == 0 or d < dmin:
source, dmin = s, d
xc, yc = source.getCentroid()
# residuals using spatial model
try:
subtractPsf(psf, im, xc, yc)
except:
continue
resid = im
if variance:
resid = resid.getImage()
var = im.getVariance()
var = var.Factory(var, True)
numpy.sqrt(var.getArray(), var.getArray()) # inplace sqrt
resid /= var
im_resid.append(resid)
# Fit the PSF components directly to the data (i.e. ignoring the spatial model)
if fitBasisComponents:
im = cand.getMaskedImage()
im = im.Factory(im, True)
im.setXY0(cand.getMaskedImage().getXY0())
try:
noSpatialKernel = psf.getKernel()
except:
noSpatialKernel = None
if noSpatialKernel:
candCenter = afwGeom.PointD(cand.getXCenter(), cand.getYCenter())
fit = fitKernelParamsToImage(noSpatialKernel, im, candCenter)
params = fit[0]
kernels = afwMath.KernelList(fit[1])
outputKernel = afwMath.LinearCombinationKernel(kernels, params)
outImage = afwImage.ImageD(outputKernel.getDimensions())
outputKernel.computeImage(outImage, False)
im -= outImage.convertF()
resid = im
if margin > 0:
bim = im.Factory(w + 2*margin, h + 2*margin)
afwMath.randomGaussianImage(bim.getImage(), afwMath.Random())
bim *= stdev
bim.assign(resid, bbox)
resid = bim
if variance:
resid = resid.getImage()
resid /= var
im_resid.append(resid)
im = im_resid.makeMosaic()
else:
im = cand.getMaskedImage()
if normalize:
im /= afwMath.makeStatistics(im, afwMath.MAX).getValue()
objId = splitId(cand.getSource().getId(), True)["objId"]
if psf:
lab = "%d chi^2 %.1f" % (objId, rchi2)
ctype = ds9.RED if cand.isBad() else ds9.GREEN
else:
lab = "%d flux %8.3g" % (objId, cand.getSource().getPsfFlux())
ctype = ds9.GREEN
mos.append(im, lab, ctype)
if False and numpy.isnan(rchi2):
ds9.mtv(cand.getMaskedImage().getImage(), title="candidate", frame=1)
print("amp", cand.getAmplitude())
im = cand.getMaskedImage()
center = (candidateIndex, xc - im.getX0(), yc - im.getY0())
candidateIndex += 1
if cand.isBad():
candidateCentersBad.append(center)
else:
candidateCenters.append(center)
if variance:
title = "chi(Psf fit)"
else:
title = "Stars & residuals"
mosaicImage = mos.makeMosaic(frame=frame, title=title)
with ds9.Buffering():
for centers, color in ((candidateCenters, ds9.GREEN), (candidateCentersBad, ds9.RED)):
for cen in centers:
bbox = mos.getBBox(cen[0])
ds9.dot("+", cen[1] + bbox.getMinX(), cen[2] + bbox.getMinY(), frame=frame, ctype=color)
return mosaicImage
def testDetectorTime(self):
"""Test that we can ask a calib for the MidTime at a point in a detector (ticket #1337)"""
p = afwGeom.PointI(3, 4)
self.calib.getMidTime(self.detector, p)
def setUp(self):
width, height = 100, 300
self.mi = afwImage.MaskedImageF(afwGeom.ExtentI(width, height))
self.mi.set(0)
self.mi.getVariance().set(10)
self.mi.getMask().addMaskPlane("DETECTED")
self.FWHM = 5
self.ksize = 25 # size of desired kernel
self.exposure = afwImage.makeExposure(self.mi)
psf = roundTripPsf(
2,
algorithms.DoubleGaussianPsf(
self.ksize, self.ksize,
self.FWHM / (2 * math.sqrt(2 * math.log(2))), 1, 0.1))
self.exposure.setPsf(psf)
for x, y in [
(20, 20),
#(30, 35), (50, 50),
(60, 20),
(60, 210),
(20, 210)
]:
flux = 10000 - 0 * x - 10 * y
sigma = 3 + 0.01 * (y - self.mi.getHeight() / 2)
psf = roundTripPsf(
3,
algorithms.DoubleGaussianPsf(self.ksize, self.ksize, sigma, 1,
0.1))
im = psf.computeImage().convertF()
im *= flux
x0y0 = afwGeom.PointI(x - self.ksize // 2, y - self.ksize // 2)
smi = self.mi.getImage().Factory(
self.mi.getImage(),
afwGeom.BoxI(x0y0, afwGeom.ExtentI(self.ksize)),
afwImage.LOCAL)
if False: # Test subtraction with non-centered psfs
im = afwMath.offsetImage(im, 0.5, 0.5)
smi += im
del psf
del im
del smi
roundTripPsf(
4,
algorithms.DoubleGaussianPsf(
self.ksize, self.ksize,
self.FWHM / (2 * math.sqrt(2 * math.log(2))), 1, 0.1))
self.cellSet = afwMath.SpatialCellSet(
afwGeom.BoxI(afwGeom.PointI(0, 0), afwGeom.ExtentI(width, height)),
100)
ds = afwDetection.FootprintSet(self.mi, afwDetection.Threshold(10),
"DETECTED")
#
# Prepare to measure
#
schema = afwTable.SourceTable.makeMinimalSchema()
sfm_config = measBase.SingleFrameMeasurementConfig()
sfm_config.plugins = [
"base_SdssCentroid", "base_CircularApertureFlux", "base_PsfFlux",
"base_SdssShape", "base_GaussianFlux", "base_PixelFlags"
]
sfm_config.slots.centroid = "base_SdssCentroid"
sfm_config.slots.shape = "base_SdssShape"
sfm_config.slots.psfFlux = "base_PsfFlux"
sfm_config.slots.instFlux = None
sfm_config.slots.apFlux = "base_CircularApertureFlux_3_0"
sfm_config.slots.modelFlux = "base_GaussianFlux"
sfm_config.slots.calibFlux = None
sfm_config.plugins["base_SdssShape"].maxShift = 10.0
sfm_config.plugins["base_CircularApertureFlux"].radii = [3.0]
task = measBase.SingleFrameMeasurementTask(schema, config=sfm_config)
measCat = afwTable.SourceCatalog(schema)
# detect the sources and run with the measurement task
ds.makeSources(measCat)
task.run(measCat, self.exposure)
for source in measCat:
self.cellSet.insertCandidate(
algorithms.makePsfCandidate(source, self.exposure))
def _fillVisitCatalog(self, butler, visitCat, srcVisits, srcCcds):
"""
Fill the visit catalog with visit metadata
Parameters
----------
butler: `lsst.daf.persistence.Butler`
visitCat: `afw.table.BaseCatalog`
Catalog with schema from _createFgcmVisitSchema()
srcVisits: `list'
List of source visits
srcCcds: `list`
List of source CCDs
"""
bbox = afwGeom.BoxI(afwGeom.PointI(0, 0), afwGeom.PointI(1, 1))
# now loop over visits and get the information
for i, srcVisit in enumerate(srcVisits):
# We don't use the bypasses since we need the psf info which does
# not have a bypass
# TODO: When DM-15500 is implemented in the Gen3 Butler, this
# can be fixed
dataId = {
self.config.visitDataRefName: srcVisit,
self.config.ccdDataRefName: srcCcds[i]
}
exp = butler.get('calexp_sub',
dataId=dataId,
bbox=bbox,
flags=afwTable.SOURCE_IO_NO_FOOTPRINTS)
visitInfo = exp.getInfo().getVisitInfo()
f = exp.getFilter()
psf = exp.getPsf()
rec = visitCat.addNew()
rec['visit'] = srcVisit
rec['filtername'] = f.getName()
radec = visitInfo.getBoresightRaDec()
rec['telra'] = radec.getRa().asDegrees()
rec['teldec'] = radec.getDec().asDegrees()
rec['telha'] = visitInfo.getBoresightHourAngle().asDegrees()
rec['mjd'] = visitInfo.getDate().get(system=DateTime.MJD)
rec['exptime'] = visitInfo.getExposureTime()
# convert from Pa to millibar
# Note that I don't know if this unit will need to be per-camera config
rec['pmb'] = visitInfo.getWeather().getAirPressure() / 100
# Flag to signify if this is a "deep" field. Not currently used
rec['deepFlag'] = 0
# Relative flat scaling (1.0 means no relative scaling)
rec['scaling'][:] = 1.0
# Median delta aperture, to be measured from stars
rec['deltaAper'] = 0.0
rec['psfSigma'] = psf.computeShape().getDeterminantRadius()
if butler.datasetExists('calexpBackground', dataId=dataId):
# Get background for reference CCD
# This approximation is good enough for now
bgStats = (
bg[0].getStatsImage().getImage().array
for bg in butler.get('calexpBackground', dataId=dataId))
rec['skyBackground'] = sum(
np.median(bg[np.isfinite(bg)]) for bg in bgStats)
else:
rec['skyBackground'] = -1.0
def setUp(self):
config = SingleFrameMeasurementTask.ConfigClass()
config.slots.apFlux = 'base_CircularApertureFlux_12_0'
self.schema = afwTable.SourceTable.makeMinimalSchema()
self.measureSources = SingleFrameMeasurementTask(self.schema,
config=config)
width, height = 110, 301
self.mi = afwImage.MaskedImageF(afwGeom.ExtentI(width, height))
self.mi.set(0)
sd = 3 # standard deviation of image
self.mi.getVariance().set(sd * sd)
self.mi.getMask().addMaskPlane("DETECTED")
self.ksize = 31 # size of desired kernel
sigma1 = 1.75
sigma2 = 2 * sigma1
self.exposure = afwImage.makeExposure(self.mi)
self.exposure.setPsf(
measAlg.DoubleGaussianPsf(self.ksize, self.ksize, 1.5 * sigma1, 1,
0.1))
cdMatrix = np.array([1.0, 0.0, 0.0, 1.0])
cdMatrix.shape = (2, 2)
wcs = afwGeom.makeSkyWcs(crpix=afwGeom.PointD(0, 0),
crval=afwGeom.SpherePoint(
0.0, 0.0, afwGeom.degrees),
cdMatrix=cdMatrix)
self.exposure.setWcs(wcs)
#
# Make a kernel with the exactly correct basis functions. Useful for debugging
#
basisKernelList = []
for sigma in (sigma1, sigma2):
basisKernel = afwMath.AnalyticKernel(
self.ksize, self.ksize,
afwMath.GaussianFunction2D(sigma, sigma))
basisImage = afwImage.ImageD(basisKernel.getDimensions())
basisKernel.computeImage(basisImage, True)
basisImage /= np.sum(basisImage.getArray())
if sigma == sigma1:
basisImage0 = basisImage
else:
basisImage -= basisImage0
basisKernelList.append(afwMath.FixedKernel(basisImage))
order = 1 # 1 => up to linear
spFunc = afwMath.PolynomialFunction2D(order)
exactKernel = afwMath.LinearCombinationKernel(basisKernelList, spFunc)
exactKernel.setSpatialParameters([[1.0, 0, 0],
[0.0, 0.5 * 1e-2, 0.2e-2]])
rand = afwMath.Random() # make these tests repeatable by setting seed
addNoise = True
if addNoise:
im = self.mi.getImage()
afwMath.randomGaussianImage(im, rand) # N(0, 1)
im *= sd # N(0, sd^2)
del im
xarr, yarr = [], []
for x, y in [
(20, 20),
(60, 20),
(30, 35),
(50, 50),
(20, 90),
(70, 160),
(25, 265),
(75, 275),
(85, 30),
(50, 120),
(70, 80),
(60, 210),
(20, 210),
]:
xarr.append(x)
yarr.append(y)
for x, y in zip(xarr, yarr):
dx = rand.uniform() - 0.5 # random (centered) offsets
dy = rand.uniform() - 0.5
k = exactKernel.getSpatialFunction(1)(
x, y) # functional variation of Kernel ...
b = (k * sigma1**2 / ((1 - k) * sigma2**2)
) # ... converted double Gaussian's "b"
#flux = 80000 - 20*x - 10*(y/float(height))**2
flux = 80000 * (1 + 0.1 * (rand.uniform() - 0.5))
I0 = flux * (1 + b) / (2 * np.pi * (sigma1**2 + b * sigma2**2))
for iy in range(y - self.ksize // 2, y + self.ksize // 2 + 1):
if iy < 0 or iy >= self.mi.getHeight():
continue
for ix in range(x - self.ksize // 2, x + self.ksize // 2 + 1):
if ix < 0 or ix >= self.mi.getWidth():
continue
I = I0 * psfVal(ix, iy, x + dx, y + dy, sigma1, sigma2, b)
Isample = rand.poisson(I) if addNoise else I
self.mi.getImage().set(
ix, iy,
self.mi.getImage().get(ix, iy) + Isample)
self.mi.getVariance().set(
ix, iy,
self.mi.getVariance().get(ix, iy) + I)
bbox = afwGeom.BoxI(afwGeom.PointI(0, 0),
afwGeom.ExtentI(width, height))
self.cellSet = afwMath.SpatialCellSet(bbox, 100)
self.footprintSet = afwDetection.FootprintSet(
self.mi, afwDetection.Threshold(100), "DETECTED")
self.catalog = self.measure(self.footprintSet, self.exposure)
for source in self.catalog:
try:
cand = measAlg.makePsfCandidate(source, self.exposure)
self.cellSet.insertCandidate(cand)
except Exception as e:
print(e)
continue
def runDataRef(self, expRefList, butler):
"""Make summary plots of full focalplane images.
"""
if len(expRefList) == 0:
return pipeBase.Struct(exitStatus=1)
lsst.afw.fits.setAllowImageCompression(
self.config.allowFitsCompression)
dstype = expRefList[0].butlerSubset.datasetType
if dstype == "raw":
def callback(im, ccd, imageSource):
return cgu.rawCallback(im,
ccd,
imageSource,
correctGain=True,
subtractBias=True)
elif dstype == "eimage":
callback = eimageCallback
elif self.config.doApplySkyCorr:
callback = skyCorrCallback
else:
callback = None
for visit in set([er.dataId["visit"] for er in expRefList]):
self.log.info("Processing visit %d", visit)
expRefListForVisit = [
er for er in expRefList if er.dataId["visit"] == visit
]
dataId = expRefListForVisit[0].dataId
bi = cgu.ButlerImage(butler,
dstype,
visit=visit,
callback=callback,
verbose=True)
if self.config.doSensorImages:
for dataId in (er.dataId for er in expRefListForVisit):
ccd = butler.get('calexp_detector', **dataId)
try:
md = butler.get('calexp_md', **dataId)
except RuntimeError:
md = None
if md:
afwGeom.makeSkyWcs(
md, strip=True
) # strip WCS cards; they're invalidated by binning
try:
binned_im = bi.getCcdImage(
ccd,
binSize=self.config.sensorBinSize,
asMaskedImage=True)[0]
binned_im = rotateImageBy90(
binned_im,
ccd.getOrientation().getNQuarter())
if self.config.putFullSensors:
binned_exp = afwImage.ExposureF(binned_im)
binned_exp.setMetadata(md)
butler.put(binned_exp,
'binned_sensor_fits',
**dataId,
dstype=dstype)
except (TypeError, RuntimeError) as e:
# butler couldn't put the image or there was no image to put
self.log.warn("Unable to make binned image: %s", e)
continue
(x, y) = binned_im.getDimensions()
boxes = {
'A':
afwGeom.Box2I(afwGeom.PointI(0, y / 2),
afwGeom.ExtentI(x, y / 2)),
'B':
afwGeom.Box2I(afwGeom.PointI(0, 0),
afwGeom.ExtentI(x, y / 2))
}
for half in ('A', 'B'):
box = boxes[half]
binned_exp = afwImage.ExposureF(binned_im[box])
binned_exp.setMetadata(md)
butler.put(binned_exp,
'binned_sensor_fits_halves',
half=half,
**dataId,
dstype=dstype)
im = cgu.showCamera(butler.get('camera'),
imageSource=bi,
binSize=self.config.binSize)
dstypeName = "%s-%s" % (
dstype, self.config.fpId) if self.config.fpId else dstype
butler.put(im, 'focal_plane_fits', visit=visit, dstype=dstypeName)
# Compute the zscale stretch for just the CCDs that have data.
detectorNameList = [
"%s_%s" % (er.dataId["raftName"], er.dataId["detectorName"])
for er in expRefListForVisit
]
im_scaling = cgu.showCamera(butler.get('camera'),
imageSource=bi,
binSize=self.config.binSize,
detectorNameList=detectorNameList)
zmap = ZScaleMapping(im_scaling, contrast=self.config.contrast)
im = flipImage(im, False, True)
rgb = zmap.makeRgbImage(im, im, im)
file_name = butler.get('focal_plane_png_filename',
visit=visit,
dstype=dstypeName)
writeRGB(file_name[0], rgb)
return pipeBase.Struct(exitStatus=0)
