def initData(shape, coords, amplitudes=None, convolve=True):
"""Initialize data for the tests
"""
B, Ny, Nx = shape
K = len(coords)
if amplitudes is None:
amplitudes = np.ones((K, ))
assert K == len(amplitudes)
_seds = [
np.arange(B, dtype=float),
np.arange(B, dtype=float)[::-1],
np.ones((B, ), dtype=float)
]
seds = np.array([_seds[n % 3] * amplitudes[n] for n in range(K)],
dtype=np.float32)
morphs = np.zeros((K, Ny, Nx), dtype=np.float32)
for k, coord in enumerate(coords):
morphs[k, coord[0], coord[1]] = 1
images = seds.T.dot(morphs.reshape(K, -1)).reshape(shape)
if convolve:
psfRadius = 20
psfShape = (2 * psfRadius + 1, 2 * psfRadius + 1)
targetPsf = GaussianPsf(psfShape[1], psfShape[0], .9)
targetPsfImage = targetPsf.computeImage().array
psfs = [
GaussianPsf(psfShape[1], psfShape[0], 1 + .2 * b) for b in range(B)
]
psfImages = np.array([psf.computeImage().array for psf in psfs])
psfImages /= psfImages.max(axis=(1, 2))[:, None, None]
# Convolve the image with the psf in each channel
# Use scipy.signal.convolve without using FFTs as a sanity check
images = np.array([
scipy.signal.convolve(img, psf, method="direct", mode="same")
for img, psf in zip(images, psfImages)
])
# Convolve the true morphology with the target PSF,
# also using scipy.signal.convolve as a sanity check
morphs = np.array([
scipy.signal.convolve(m,
targetPsfImage,
method="direct",
mode="same") for m in morphs
])
morphs /= morphs.max()
psfImages /= psfImages.sum(axis=(1, 2))[:, None, None]
channels = range(len(images))
return targetPsfImage, psfImages, images, channels, seds, morphs, targetPsf, psfs
def testMultiPlaneFitsReaders(self):
"""Run tests for MaskedImageFitsReader and ExposureFitsReader.
"""
metadata = PropertyList()
metadata.add("FIVE", 5)
metadata.add("SIX", 6.0)
wcs = makeSkyWcs(Point2D(2.5, 3.75),
SpherePoint(40.0 * degrees, 50.0 * degrees),
np.array([[1E-5, 0.0], [0.0, -1E-5]]))
defineFilter("test_readers_filter", lambdaEff=470.0)
calib = PhotoCalib(2.5E4)
psf = GaussianPsf(21, 21, 8.0)
polygon = Polygon(Box2D(self.bbox))
apCorrMap = ApCorrMap()
visitInfo = VisitInfo(exposureTime=5.0)
transmissionCurve = TransmissionCurve.makeIdentity()
coaddInputs = CoaddInputs(ExposureTable.makeMinimalSchema(),
ExposureTable.makeMinimalSchema())
detector = DetectorWrapper().detector
record = coaddInputs.ccds.addNew()
record.setWcs(wcs)
record.setPhotoCalib(calib)
record.setPsf(psf)
record.setValidPolygon(polygon)
record.setApCorrMap(apCorrMap)
record.setVisitInfo(visitInfo)
record.setTransmissionCurve(transmissionCurve)
record.setDetector(detector)
for n, dtypeIn in enumerate(self.dtypes):
with self.subTest(dtypeIn=dtypeIn):
exposureIn = Exposure(self.bbox, dtype=dtypeIn)
shape = exposureIn.image.array.shape
exposureIn.image.array[:, :] = np.random.randint(low=1,
high=5,
size=shape)
exposureIn.mask.array[:, :] = np.random.randint(low=1,
high=5,
size=shape)
exposureIn.variance.array[:, :] = np.random.randint(low=1,
high=5,
size=shape)
exposureIn.setMetadata(metadata)
exposureIn.setWcs(wcs)
exposureIn.setFilter(Filter("test_readers_filter"))
exposureIn.setFilterLabel(
FilterLabel(physical="test_readers_filter"))
exposureIn.setPhotoCalib(calib)
exposureIn.setPsf(psf)
exposureIn.getInfo().setValidPolygon(polygon)
exposureIn.getInfo().setApCorrMap(apCorrMap)
exposureIn.getInfo().setVisitInfo(visitInfo)
exposureIn.getInfo().setTransmissionCurve(transmissionCurve)
exposureIn.getInfo().setCoaddInputs(coaddInputs)
exposureIn.setDetector(detector)
with lsst.utils.tests.getTempFilePath(".fits") as fileName:
exposureIn.writeFits(fileName)
self.checkMaskedImageFitsReader(exposureIn, fileName,
self.dtypes[n:])
self.checkExposureFitsReader(exposureIn, fileName,
self.dtypes[n:])
def setUp(self):
np.random.seed(1)
self.bbox = Box2I(Point2I(1000, 2000), Extent2I(200, 100))
self.filters = ["G", "R", "I"]
self.imgValue = 10
images = [ImageF(self.bbox, self.imgValue) for f in self.filters]
mImage = MultibandImage.fromImages(self.filters, images)
self.Mask = Mask[MaskPixel]
# Store the default mask planes for later use
maskPlaneDict = self.Mask().getMaskPlaneDict()
self.defaultMaskPlanes = sorted(maskPlaneDict, key=maskPlaneDict.__getitem__)
# reset so tests will be deterministic
self.Mask.clearMaskPlaneDict()
for p in ("BAD", "SAT", "INTRP", "CR", "EDGE"):
self.Mask.addMaskPlane(p)
self.maskValue = self.Mask.getPlaneBitMask("BAD")
singles = [self.Mask(self.bbox) for f in range(len(self.filters))]
for n in range(len(singles)):
singles[n].set(self.maskValue)
mMask = MultibandMask.fromMasks(self.filters, singles)
self.varValue = 1e-2
images = [ImageF(self.bbox, self.varValue) for f in self.filters]
mVariance = MultibandImage.fromImages(self.filters, images)
self.kernelSize = 51
self.psfs = [GaussianPsf(self.kernelSize, self.kernelSize, 4.0) for f in self.filters]
self.psfImage = np.array([p.computeKernelImage().array for p in self.psfs])
self.exposure = MultibandExposure(image=mImage, mask=mMask, variance=mVariance,
psfs=self.psfs, filters=self.filters)
def makeExposure(self, universalId):
"""Make a tiny exposure with exposure info set, but no pixels
In particular, exposure info is set as a record in a table, so it can be recorded in a coadd
"""
inputRecorder = self.coaddInputRecorder.makeCoaddTempExpRecorder(universalId, self.numExp)
bbox = lsst.afw.geom.Box2I(lsst.afw.geom.Point2I(100, 100), lsst.afw.geom.Extent2I(10, 10))
detectorName = "detector {}".format(universalId)
detector = lsst.afw.cameraGeom.testUtils.DetectorWrapper(name=detectorName, id=universalId).detector
exp = lsst.afw.image.ExposureF(bbox)
exp.setDetector(detector)
expInfo = exp.getInfo()
scale = 5.1e-5*lsst.afw.geom.degrees
cdMatrix = lsst.afw.geom.makeCdMatrix(scale=scale)
wcs = lsst.afw.geom.makeSkyWcs(
crpix=lsst.afw.geom.Point2D(5, 5),
crval=lsst.afw.geom.SpherePoint(10, 45, lsst.afw.geom.degrees),
cdMatrix=cdMatrix,
)
expInfo.setWcs(wcs)
expInfo.setPsf(GaussianPsf(5, 5, 2.5))
expInfo.setPhotoCalib(lsst.afw.image.makePhotoCalibFromCalibZeroPoint(1.1e12, 2.2e10))
expInfo.setApCorrMap(self.makeApCorrMap())
expInfo.setValidPolygon(lsst.afw.geom.Polygon(lsst.afw.geom.Box2D(bbox).getCorners()))
if self.version > 1:
expInfo.setVisitInfo(self.makeVisitInfo())
inputRecorder.addCalExp(calExp=exp, ccdId=universalId, nGoodPix=100)
inputRecorder.finish(coaddTempExp=exp, nGoodPix=100)
return exp
def testPsf(self):
psfImage = self.exposure.computePsfKernelImage().array
self.assertFloatsAlmostEqual(psfImage, self.psfImage)
newPsfs = [GaussianPsf(self.kernelSize, self.kernelSize, 1.0) for f in self.filters]
newPsfImage = [p.computeImage().array for p in newPsfs]
for psf, exposure in zip(newPsfs, self.exposure.singles):
exposure.setPsf(psf)
psfImage = self.exposure.computePsfKernelImage()
self.assertFloatsAlmostEqual(psfImage.array, newPsfImage)
psfImage = self.exposure.computePsfImage().array[0]
self.assertFloatsAlmostEqual(psfImage, self.exposure["G"].getPsf().computeImage().array)
def loadData(psfSigma=1.5):
"""Prepare the data we need to run the example"""
# Load sample input from disk
mypath = lsst.utils.getPackageDir('afwdata')
imFile = os.path.join(mypath, "CFHT", "D4",
"cal-53535-i-797722_small_1.fits")
exposure = afwImage.ExposureF(imFile)
# add a filter
afwImage.Filter.define(afwImage.FilterProperty(FilterName, 600, True))
exposure.setFilter(afwImage.Filter(FilterName))
# add a simple Gaussian PSF model
psfModel = GaussianPsf(11, 11, psfSigma)
exposure.setPsf(psfModel)
return exposure
def makeExposure(self, universalId):
"""Make a tiny exposure with exposure info set, but no pixels
In particular, exposure info is set as a record in a table, so it can be recorded in a coadd
"""
inputRecorder = self.coaddInputRecorder.makeCoaddTempExpRecorder(
universalId, self.numExp)
bbox = lsst.afw.geom.Box2I(lsst.afw.geom.Point2I(100, 100),
lsst.afw.geom.Extent2I(10, 10))
detectorName = "detector {}".format(universalId)
detector = lsst.afw.cameraGeom.testUtils.DetectorWrapper(
name=detectorName, id=universalId).detector
exp = lsst.afw.image.ExposureF(bbox)
exp.setDetector(detector)
expInfo = exp.getInfo()
wcs = lsst.afw.image.makeWcs(
IcrsCoord(10 * lsst.afw.geom.degrees, 45 * lsst.afw.geom.degrees),
lsst.afw.geom.Point2D(5, 5),
5.1e-5,
0,
0,
-5.1e-5, # CD: 11, 12, 21, 22
)
expInfo.setWcs(wcs)
expInfo.setPsf(GaussianPsf(5, 5, 2.5))
calib = lsst.afw.image.Calib()
calib.setFluxMag0(1.1e12, 2.2e10)
expInfo.setCalib(calib)
expInfo.setApCorrMap(self.makeApCorrMap())
expInfo.setValidPolygon(Polygon(
lsst.afw.geom.Box2D(bbox).getCorners()))
if self.version > 1:
expInfo.setVisitInfo(self.makeVisitInfo())
inputRecorder.addCalExp(calExp=exp, ccdId=universalId, nGoodPix=100)
inputRecorder.finish(coaddTempExp=exp, nGoodPix=100)
return exp
def setUp(self):
self.pgps = []
self.gps = []
for width, height, sigma in [(5, 5, 1.1), (5, 3, 1.2), (7, 7, 1.3)]:
self.pgps.append(PyGaussianPsf(width, height, sigma))
self.gps.append(GaussianPsf(width, height, sigma))
def testComputeExposureSummary(self):
"""Make a fake exposure and background and compute summary.
"""
np.random.seed(12345)
# Make an exposure with a noise image
exposure = afwImage.ExposureF(100, 100)
skySigma = 10.0
exposure.getImage().getArray()[:, :] = np.random.normal(0.0,
skySigma,
size=(100,
100))
exposure.getVariance().getArray()[:, :] = skySigma**2.
# Set the visitInfo
date = DateTime(date=59234.7083333334, system=DateTime.DateSystem.MJD)
observatory = Observatory(-70.7366 * lsst.geom.degrees,
-30.2407 * lsst.geom.degrees, 2650.)
visitInfo = afwImage.VisitInfo(exposureTime=10.0,
date=date,
observatory=observatory)
exposure.getInfo().setVisitInfo(visitInfo)
# Install a Gaussian PSF
psfSize = 2.0
psf = GaussianPsf(5, 5, psfSize)
exposure.setPsf(psf)
# Install a simple WCS
scale = 0.2 * lsst.geom.arcseconds
raCenter = 300.0 * lsst.geom.degrees
decCenter = 0.0 * lsst.geom.degrees
cdMatrix = makeCdMatrix(scale=scale)
skyWcs = makeSkyWcs(crpix=exposure.getBBox().getCenter(),
crval=lsst.geom.SpherePoint(raCenter, decCenter),
cdMatrix=cdMatrix)
exposure.setWcs(skyWcs)
# Install a simple photoCalib
photoCalib = afwImage.PhotoCalib(calibrationMean=0.3)
zp = 2.5 * np.log10(photoCalib.getInstFluxAtZeroMagnitude())
exposure.setPhotoCalib(photoCalib)
# Compute the background image
bgGridSize = 10
bctrl = afwMath.BackgroundControl(afwMath.Interpolate.NATURAL_SPLINE)
bctrl.setNxSample(
int(exposure.getMaskedImage().getWidth() / bgGridSize) + 1)
bctrl.setNySample(
int(exposure.getMaskedImage().getHeight() / bgGridSize) + 1)
backobj = afwMath.makeBackground(exposure.getMaskedImage().getImage(),
bctrl)
background = afwMath.BackgroundList()
background.append(backobj)
# Run the task
expSummaryTask = ComputeExposureSummaryStatsTask()
summary = expSummaryTask.run(exposure, None, background)
# Test the outputs
self.assertFloatsAlmostEqual(summary.psfSigma, psfSize)
self.assertFloatsAlmostEqual(summary.psfIxx, psfSize**2.)
self.assertFloatsAlmostEqual(summary.psfIyy, psfSize**2.)
self.assertFloatsAlmostEqual(summary.psfIxy, 0.0)
self.assertFloatsAlmostEqual(summary.psfArea, 23.088975164455444)
delta = (scale * 50).asDegrees()
for a, b in zip(summary.raCorners, [
raCenter.asDegrees() + delta,
raCenter.asDegrees() - delta,
raCenter.asDegrees() - delta,
raCenter.asDegrees() + delta
]):
self.assertFloatsAlmostEqual(a, b, atol=1e-10)
for a, b in zip(summary.decCorners, [
decCenter.asDegrees() - delta,
decCenter.asDegrees() - delta,
decCenter.asDegrees() + delta,
decCenter.asDegrees() + delta
]):
self.assertFloatsAlmostEqual(a, b, atol=1e-10)
self.assertFloatsAlmostEqual(summary.ra,
raCenter.asDegrees(),
atol=1e-10)
self.assertFloatsAlmostEqual(summary.decl,
decCenter.asDegrees(),
atol=1e-10)
self.assertFloatsAlmostEqual(summary.zeroPoint, zp)
# Need to compare background level and noise
# These are only approximately 0+/-10 because of the small image
self.assertFloatsAlmostEqual(summary.skyBg, -0.079, atol=1e-3)
self.assertFloatsAlmostEqual(summary.meanVar, skySigma**2.)
self.assertFloatsAlmostEqual(summary.zenithDistance,
30.57112,
atol=1e-5)
def makeMockVisitSummary(visit,
ra_center=0.0,
dec_center=-45.0,
physical_filter='TEST-I',
band='i',
mjd=59234.7083333334,
psf_sigma=3.0,
zenith_distance=45.0,
zero_point=30.0,
sky_background=100.0,
sky_noise=10.0,
mean_var=100.0,
exposure_time=100.0,
detector_size=200,
pixel_scale=0.2):
"""Make a mock visit summary catalog.
This will contain two square detectors with the same metadata,
with a small (20 pixel) gap between the detectors. There is no
rotation, as each detector is simply offset in RA from the
specified boresight.
Parameters
----------
visit : `int`
Visit number.
ra_center : `float`
Right ascension of the center of the "camera" boresight (degrees).
dec_center : `float`
Declination of the center of the "camera" boresight (degrees).
physical_filter : `str`
Arbitrary name for the physical filter.
band : `str`
Name of the associated band.
mjd : `float`
Modified Julian Date.
psf_sigma : `float`
Sigma width of Gaussian psf.
zenith_distance : `float`
Distance from zenith of the visit (degrees).
zero_point : `float`
Constant zero point for the visit (magnitudes).
sky_background : `float`
Background level for the visit (counts).
sky_noise : `float`
Noise level for the background of the visit (counts).
mean_var : `float`
Mean of the variance plane of the visit (counts).
exposure_time : `float`
Exposure time of the visit (seconds).
detector_size : `int`
Size of each square detector in the visit (pixels).
pixel_scale : `float`
Size of the pixel in arcseconds per pixel.
Returns
-------
visit_summary : `lsst.afw.table.ExposureCatalog`
"""
# We are making a 2 detector "camera"
n_detector = 2
schema = ConsolidateVisitSummaryTask()._makeVisitSummarySchema()
visit_summary = afwTable.ExposureCatalog(schema)
visit_summary.resize(n_detector)
bbox = geom.Box2I(x=geom.IntervalI(min=0, max=detector_size - 1),
y=geom.IntervalI(min=0, max=detector_size - 1))
for detector_id in range(n_detector):
row = visit_summary[detector_id]
row['id'] = detector_id
row.setBBox(bbox)
row['visit'] = visit
row['physical_filter'] = physical_filter
row['band'] = band
row['zenithDistance'] = zenith_distance
row['zeroPoint'] = zero_point
row['skyBg'] = sky_background
row['skyNoise'] = sky_noise
row['meanVar'] = mean_var
# Generate a photocalib
instFluxMag0 = 10.**(zero_point / 2.5)
row.setPhotoCalib(
afwImage.makePhotoCalibFromCalibZeroPoint(instFluxMag0))
# Generate a WCS and set values accordingly
crpix = geom.Point2D(detector_size / 2., detector_size / 2.)
# Create a 20 pixel gap between the two detectors (each offset 10 pixels).
if detector_id == 0:
delta_ra = -1.0 * ((detector_size + 10) * pixel_scale /
3600.) / np.cos(np.deg2rad(dec_center))
delta_dec = 0.0
elif detector_id == 1:
delta_ra = ((detector_size + 10) * pixel_scale / 3600.) / np.cos(
np.deg2rad(dec_center))
delta_dec = 0.0
crval = geom.SpherePoint(ra_center + delta_ra, dec_center + delta_dec,
geom.degrees)
cd_matrix = afwGeom.makeCdMatrix(scale=pixel_scale * geom.arcseconds,
orientation=0.0 * geom.degrees)
wcs = afwGeom.makeSkyWcs(crpix=crpix, crval=crval, cdMatrix=cd_matrix)
row.setWcs(wcs)
sph_pts = wcs.pixelToSky(geom.Box2D(bbox).getCorners())
row['raCorners'] = np.array(
[float(sph.getRa().asDegrees()) for sph in sph_pts])
row['decCorners'] = np.array(
[float(sph.getDec().asDegrees()) for sph in sph_pts])
sph_pt = wcs.pixelToSky(bbox.getCenter())
row['ra'] = sph_pt.getRa().asDegrees()
row['decl'] = sph_pt.getDec().asDegrees()
# Generate a visitInfo.
# This does not need to be consistent with the zenith angle in the table,
# it just needs to be valid and have sufficient information to compute
# exposure time and parallactic angle.
date = DateTime(date=mjd, system=DateTime.DateSystem.MJD)
visit_info = afwImage.VisitInfo(
exposureId=visit,
exposureTime=exposure_time,
date=date,
darkTime=0.0,
boresightRaDec=geom.SpherePoint(ra_center, dec_center,
geom.degrees),
era=45.1 * geom.degrees,
observatory=Observatory(11.1 * geom.degrees, 0.0 * geom.degrees,
0.333),
boresightRotAngle=0.0 * geom.degrees,
rotType=afwImage.RotType.SKY)
row.setVisitInfo(visit_info)
# Generate a PSF and set values accordingly
psf = GaussianPsf(15, 15, psf_sigma)
row.setPsf(psf)
psfAvgPos = psf.getAveragePosition()
shape = psf.computeShape(psfAvgPos)
row['psfSigma'] = psf.getSigma()
row['psfIxx'] = shape.getIxx()
row['psfIyy'] = shape.getIyy()
row['psfIxy'] = shape.getIxy()
row['psfArea'] = shape.getArea()
return visit_summary