def measureAndNormalize(
self,
annulus,
statsControl=afwMath.StatisticsControl(),
statsFlag=afwMath.stringToStatisticsProperty("MEAN"),
badMaskPlanes=('BAD', 'SAT', 'NO_DATA')):
"""Compute "annularFlux", the integrated flux within an annulus
around an object's center, and normalize it.
Since the center of bright stars are saturated and/or heavily affected
by ghosts, we measure their flux in an annulus with a large enough
inner radius to avoid the most severe ghosts and contain enough
non-saturated pixels.
Parameters
----------
annulus : `lsst.afw.geom.spanSet.SpanSet`
SpanSet containing the annulus to use for normalization.
statsControl : `lsst.afw.math.statistics.StatisticsControl`, optional
StatisticsControl to be used when computing flux over all pixels
within the annulus.
statsFlag : `lsst.afw.math.statistics.Property`, optional
statsFlag to be passed on to ``afwMath.makeStatistics`` to compute
annularFlux. Defaults to a simple MEAN.
badMaskPlanes : `collections.abc.Collection` [`str`]
Collection of mask planes to ignore when computing annularFlux.
"""
stampSize = self.stamp_im.getDimensions()
# create image with the same pixel values within annulus, NO_DATA
# elsewhere
maskPlaneDict = self.stamp_im.mask.getMaskPlaneDict()
annulusImage = MaskedImageF(stampSize, planeDict=maskPlaneDict)
annulusMask = annulusImage.mask
annulusMask.array[:] = 2**maskPlaneDict['NO_DATA']
annulus.copyMaskedImage(self.stamp_im, annulusImage)
# set mask planes to be ignored
andMask = reduce(ior, (annulusMask.getPlaneBitMask(bm)
for bm in badMaskPlanes))
statsControl.setAndMask(andMask)
# compute annularFlux
annulusStat = afwMath.makeStatistics(annulusImage, statsFlag,
statsControl)
self.annularFlux = annulusStat.getValue()
if np.isnan(self.annularFlux):
raise RuntimeError(
"Annular flux computation failed, likely because no pixels were valid."
)
# normalize stamps
self.stamp_im.image.array /= self.annularFlux
return None
def setUp(self):
np.random.seed(1)
self.spans = SpanSet.fromShape(2, Stencil.CIRCLE)
self.footprint = Footprint(self.spans)
self.footprint.addPeak(3, 4, 10)
self.footprint.addPeak(8, 1, 2)
fp = Footprint(self.spans)
for peak in self.footprint.getPeaks():
fp.addPeak(peak["f_x"], peak["f_y"], peak["peakValue"])
self.peaks = fp.getPeaks()
self.bbox = self.footprint.getBBox()
self.filters = ("G", "R", "I")
singles = []
images = []
for n, f in enumerate(self.filters):
image = ImageF(self.spans.getBBox())
image.set(n)
images.append(image.array)
maskedImage = MaskedImageF(image)
heavy = makeHeavyFootprint(self.footprint, maskedImage)
singles.append(heavy)
self.image = np.array(images)
self.mFoot = MultibandFootprint(self.filters, singles)
import lsst.utils
from lsst.afw.image import MaskedImageF
from lsst.pipe.tasks.exampleStatsTasks import ExampleSimpleStatsTask, ExampleSigmaClippedStatsTask
# Parse command-line arguments. If the user supplies an image, use it;
# otherwise use one from the afwdata package (or complain if afwdata is not setup).
if len(sys.argv) < 2:
afwDataDir = lsst.utils.getPackageDir('afwdata')
maskedImagePath = os.path.join(afwDataDir, "data", "med.fits")
else:
maskedImagePath = sys.argv[1]
print("computing statistics on %r\n" % (maskedImagePath, ))
# Read the masked image from the specified file. The file may be a masked image or exposure,
# but if the file is a simple image, with no mask or variance plane, then this call will fail.
maskedImage = MaskedImageF(maskedImagePath)
# Construct the simple stats task configuration and use that to construct and run the task
print("running ExampleSimpleStatsTask")
config1 = ExampleSimpleStatsTask.ConfigClass()
# ...modify the config if desired...
config1.validate(
) # check that the config parameters are valid; optional, but catches errors early
task1 = ExampleSimpleStatsTask(config=config1)
res1 = task1.run(maskedImage)
print("result =", res1)
print()
# Construct the sigma-clipped stats task configuration and use that to construct and run the task
print("running ExampleSigmaClippedStatsTask")
config2 = ExampleSigmaClippedStatsTask.ConfigClass()