def DebugInfo(name):
di = lsstDebug.getInfo(name)
#di.display = True # N.b. lsstDebug.Info(name) would call us recursively
if name == "lsst.pipe.tasks.matchBackgrounds":
di.display = False
di.savefig = True
di.savefits = False
di.figpath = "/lsst/home/yusra/figures/"
if name == "lsst.obs.sdss.selectFluxMag0":
di.display = True
if name == "lsst.ip.diffim.psfMatch":
di.display = True # global; _solve function
di.maskTransparency = 80 # ds9 mask transparency
di.displayCandidates = True # show all the candidates and residuals
di.displayKernelBasis = True # show kernel basis functions
di.displayKernelMosaic = True # show kernel realized across the image
di.plotKernelSpatialModel = False # show coefficients of spatial model
di.showBadCandidates = True # show the bad candidates (red) along with good (green)
elif name == "lsst.ip.diffim.imagePsfMatch":
di.display = False # global
di.maskTransparency = 80 # ds9 mask transparency
di.displayTemplate = True # show full (remapped) template
di.displaySciIm = True # show science image to match to
di.displaySpatialCells = True # show spatial cells
di.displayDiffIm = True # show difference image
di.showBadCandidates = True # show the bad candidates (red) along with good (green)
elif name == "lsst.pipe.tasks.imageDifference":
di.display =True # global
di.maskTransparency = 50 # ds9 mask transparency
di.showPixelResiduals = True # histograms of diffim / sqrt(variance)
di.showDiaSources = True # display diffim-detected sources
return di
def MyInfo(name):
#print 'MyInfo:', name
di = lsstDebug.getInfo(name)
#if name == 'lsst.meas.algorithms.measurement':
# di.display = True
if name == '__main__':
#di.display = True
pass
return di
def MyInfo(name):
#print 'MyInfo:', name
di = lsstDebug.getInfo(name)
#if name == 'lsst.meas.algorithms.measurement':
# di.display = True
if name == '__main__':
#di.display = True
pass
return di
def detectAndMeasureWithDM():
### what file are you going to look at? This should be in fits format.
### The test image included in this example contains stars and galaxies in the center only.
imagefile = "test_image_stars.fits"
### open the file in the stack format
exposure = afwImage.ExposureF(imagefile)
im = exposure.getMaskedImage().getImage()
### display the original image
frame = 0
ds9.mtv(exposure, frame=frame, title="Original Image"); frame+=1
### Subtract background.
### To do this, we'll set up a grid of 64x64 pixel areas across th eimage
### Fit the second moment of the pixels in each (which should be bg-dominated) and a fit a smooth function
### Subtract this function.
print "** subtracting background"
#back_size = 256 ## dunno what this is
#back_ctrl = afwMath.BackgroundControl(im.getWidth()//back_size+1, im.getHeight()//back_size +1)
#back_obj = afwMath.makeBackground(im, back_ctrl)
#im -=back_obj.getImageF("LINEAR")
im -= float(np.median(im.getArray()))
ds9.mtv(exposure, frame=frame, title="Background removed"); frame+=1
### Set up the schema
schema = afwTable.SourceTable.makeMinimalSchema()
schema.setVersion(0)
### Create the detection task
config = SourceDetectionTask.ConfigClass()
config.reEstimateBackground = False
detectionTask = SourceDetectionTask(config=config, schema=schema)
### create the measurement task
config = SourceMeasurementTask.ConfigClass()
config.slots.psfFlux = "flux.sinc" # use of the psf flux is hardcoded in secondMomentStarSelector
measureTask = SourceMeasurementTask(schema, config=config)
### Create the measurePsf task
config = MeasurePsfTask.ConfigClass()
starSelector = config.starSelector.apply()
starSelector.name = "objectSize"
#starSelector.config.badFlags = ["flags.pixel.edge", "flags.pixel.cr.center",
# "flags.pixel.interpolated.center", "flags.pixel.saturated.center"]
psfDeterminer = config.psfDeterminer.apply()
psfDeterminer.config.sizeCellX = 128
psfDeterminer.config.sizeCellY = 128
psfDeterminer.config.nStarPerCell = 1
psfDeterminer.config.spatialOrder = 1
psfDeterminer.config.nEigenComponents = 3
measurePsfTask = MeasurePsfTask(config=config, schema=schema)
### Create the output table
tab = afwTable.SourceTable.make(schema)
lsstDebug.getInfo(MeasurePsfTask).display=True
### Process the data
print "*** running detection task..."
sources = detectionTask.run(tab, exposure, sigma=2).sources
print "*** running measure task..."
measureTask.measure(exposure, sources)
print "*** running measurePsf task..."
result = measurePsfTask.run(exposure, sources)
psf = result.psf
cellSet = result.cellSet
### Look at the psf
psfIm = psf.computeImage()
ds9.mtv(psfIm, frame=frame, title = "Psf Image"); frame+=1
### render it on a grid
import lsst.meas.algorithms.utils as measUtils
cellSet = result.cellSet
measUtils.showPsfMosaic(exposure, psf=psf, frame=frame); frame += 1
with ds9.Buffering():
for s in sources:
xy = s.getCentroid()
ds9.dot('+', *xy, ctype=ds9.GREEN, frame=frame)
if s.get("calib.psf.candidate"):
ds9.dot('x', *xy, ctype=ds9.YELLOW, frame=frame)
if s.get("calib.psf.used"):
ds9.dot('o', *xy, size=4, ctype=ds9.RED, frame=frame)
"""
def DebugInfo(name):
di = lsstDebug.getInfo(
name) # N.b. lsstDebug.Info(name) would call us recursively
if name == "lsst.ip.isr.fringe":
di.plot = True
return di
def DebugInfo(name):
di = lsstDebug.getInfo(name) # N.b. lsstDebug.Info(name) would call us recursively
if name == "lsst.ip.isr.fringe":
di.plot = True
return di