# This file is part of pipe_tasks.
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from astropy import units as u
import numpy as np
import unittest
from lsst.afw.coord import Observatory, Weather
import lsst.afw.geom as afwGeom
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
from lsst.geom import arcseconds, degrees, radians
from lsst.meas.algorithms.testUtils import plantSources
import lsst.utils.tests
from lsst.pipe.tasks.dcrModel import DcrModel, calculateDcr, calculateImageParallacticAngle
class DcrModelTestTask(lsst.utils.tests.TestCase):
"""A test case for the DCR-aware image coaddition algorithm.
Attributes
----------
bbox : `lsst.afw.geom.Box2I`
Bounding box of the test model.
bufferSize : `int`
Distance from the inner edge of the bounding box
to avoid placing test sources in the model images.
dcrNumSubfilters : int
Number of sub-filters used to model chromatic effects within a band.
lambdaEff : `float`
Effective wavelength of the full band.
lambdaMax : `float`
Maximum wavelength where the relative throughput
of the band is greater than 1%.
lambdaMin : `float`
Minimum wavelength where the relative throughput
of the band is greater than 1%.
mask : `lsst.afw.image.Mask`
Reference mask of the unshifted model.
"""
def setUp(self):
"""Define the filter, DCR parameters, and the bounding box for the tests.
"""
self.dcrNumSubfilters = 3
self.lambdaEff = 476.31 # Use LSST g band values for the test.
self.lambdaMin = 405.
self.lambdaMax = 552.
self.bufferSize = 5
xSize = 40
ySize = 42
x0 = 12345
y0 = 67890
self.bbox = afwGeom.Box2I(afwGeom.Point2I(x0, y0), afwGeom.Extent2I(xSize, ySize))
def makeTestImages(self):
"""Make reproduceable PSF-convolved masked images for testing.
Returns
-------
modelImages : `list` of `lsst.afw.image.maskedImage`
A list of masked images, each containing the model for one subfilter
"""
seed = 5
rng = np.random.RandomState(seed)
psfSize = 2
nSrc = 5
noiseLevel = 5
detectionSigma = 5.
sourceSigma = 20.
fluxRange = 2.
x0, y0 = self.bbox.getBegin()
xSize, ySize = self.bbox.getDimensions()
xLoc = rng.rand(nSrc)*(xSize - 2*self.bufferSize) + self.bufferSize + x0
yLoc = rng.rand(nSrc)*(ySize - 2*self.bufferSize) + self.bufferSize + y0
modelImages = []
imageSum = np.zeros((ySize, xSize))
for subfilter in range(self.dcrNumSubfilters):
flux = (rng.rand(nSrc)*(fluxRange - 1.) + 1.)*sourceSigma*noiseLevel
sigmas = [psfSize for src in range(nSrc)]
coordList = list(zip(xLoc, yLoc, flux, sigmas))
model = plantSources(self.bbox, 10, 0, coordList, addPoissonNoise=False)
model.image.array += rng.rand(ySize, xSize)*noiseLevel
imageSum += model.image.array
model.mask.addMaskPlane("CLIPPED")
modelImages.append(model.maskedImage)
maskVals = np.zeros_like(imageSum)
maskVals[imageSum > detectionSigma*noiseLevel] = afwImage.Mask.getPlaneBitMask('DETECTED')
for model in modelImages:
model.mask.array[:] = maskVals
self.mask = modelImages[0].mask
return modelImages
def makeDummyWcs(self, rotAngle, pixelScale, crval):
"""Make a World Coordinate System object for testing.
Parameters
----------
rotAngle : `lsst.geom.Angle`
rotation of the CD matrix, East from North
pixelScale : `lsst.geom.Angle`
Pixel scale of the projection.
crval : `lsst.afw.geom.SpherePoint`
Coordinates of the reference pixel of the wcs.
Returns
-------
`lsst.afw.geom.skyWcs.SkyWcs`
A wcs that matches the inputs.
"""
crpix = afwGeom.Box2D(self.bbox).getCenter()
cdMatrix = afwGeom.makeCdMatrix(scale=pixelScale, orientation=rotAngle, flipX=True)
wcs = afwGeom.makeSkyWcs(crpix=crpix, crval=crval, cdMatrix=cdMatrix)
return wcs
def makeDummyVisitInfo(self, azimuth, elevation):
"""Make a self-consistent visitInfo object for testing.
For simplicity, the simulated observation is assumed
to be taken on the local meridian.
Parameters
----------
azimuth : `lsst.geom.Angle`
Azimuth angle of the simulated observation.
elevation : `lsst.geom.Angle`
Elevation angle of the simulated observation.
Returns
-------
`lsst.afw.image.VisitInfo`
VisitInfo for the exposure.
"""
lsstLat = -30.244639*degrees
lsstLon = -70.749417*degrees
lsstAlt = 2663.
lsstTemperature = 20.*u.Celsius # in degrees Celcius
lsstHumidity = 40. # in percent
lsstPressure = 73892.*u.pascal
lsstWeather = Weather(lsstTemperature.value, lsstPressure.value, lsstHumidity)
lsstObservatory = Observatory(lsstLon, lsstLat, lsstAlt)
airmass = 1.0/np.sin(elevation.asRadians())
era = 0.*radians # on the meridian
zenithAngle = 90.*degrees - elevation
ra = lsstLon + np.sin(azimuth.asRadians())*zenithAngle/np.cos(lsstLat.asRadians())
dec = lsstLat + np.cos(azimuth.asRadians())*zenithAngle
visitInfo = afwImage.VisitInfo(era=era,
boresightRaDec=afwGeom.SpherePoint(ra, dec),
boresightAzAlt=afwGeom.SpherePoint(azimuth, elevation),
boresightAirmass=airmass,
boresightRotAngle=0.*radians,
observatory=lsstObservatory,
weather=lsstWeather
)
return visitInfo
def testDcrCalculation(self):
"""Test that the shift in pixels due to DCR is consistently computed.
The shift is compared to pre-computed values.
"""
dcrNumSubfilters = 3
afwImage.utils.defineFilter("gTest", self.lambdaEff,
lambdaMin=self.lambdaMin, lambdaMax=self.lambdaMax)
filterInfo = afwImage.Filter("gTest")
rotAngle = 0.*radians
azimuth = 30.*degrees
elevation = 65.*degrees
pixelScale = 0.2*arcseconds
visitInfo = self.makeDummyVisitInfo(azimuth, elevation)
wcs = self.makeDummyWcs(rotAngle, pixelScale, crval=visitInfo.getBoresightRaDec())
dcrShift = calculateDcr(visitInfo, wcs, filterInfo, dcrNumSubfilters)
refShift = [afwGeom.Extent2D(-0.5363512808, -0.3103517169),
afwGeom.Extent2D(0.001887293861, 0.001092054612),
afwGeom.Extent2D(0.3886592703, 0.2248919247)]
for shiftOld, shiftNew in zip(refShift, dcrShift):
self.assertFloatsAlmostEqual(shiftOld.getX(), shiftNew.getX(), rtol=1e-6, atol=1e-8)
self.assertFloatsAlmostEqual(shiftOld.getY(), shiftNew.getY(), rtol=1e-6, atol=1e-8)
def testRotationAngle(self):
"""Test that the sky rotation angle is consistently computed.
The rotation is compared to pre-computed values.
"""
cdRotAngle = 0.*radians
azimuth = 130.*afwGeom.degrees
elevation = 70.*afwGeom.degrees
pixelScale = 0.2*afwGeom.arcseconds
visitInfo = self.makeDummyVisitInfo(azimuth, elevation)
wcs = self.makeDummyWcs(cdRotAngle, pixelScale, crval=visitInfo.getBoresightRaDec())
rotAngle = calculateImageParallacticAngle(visitInfo, wcs)
refAngle = -0.9344289857053072*radians
self.assertAnglesAlmostEqual(refAngle, rotAngle, maxDiff=1e-6*radians)
def testConditionDcrModelNoChange(self):
"""Conditioning should not change the model if it equals the reference.
This additionally tests that the variance and mask planes do not change.
"""
dcrModels = DcrModel(modelImages=self.makeTestImages())
newModels = [dcrModels[subfilter].clone() for subfilter in range(self.dcrNumSubfilters)]
for subfilter, newModel in enumerate(newModels):
dcrModels.conditionDcrModel(subfilter, newModel, self.bbox, gain=1.)
self.assertMaskedImagesEqual(dcrModels[subfilter], newModel)
def testConditionDcrModelNoChangeHighGain(self):
"""Conditioning should not change the model if it equals the reference.
This additionally tests that the variance and mask planes do not change.
"""
dcrModels = DcrModel(modelImages=self.makeTestImages())
newModels = [dcrModels[subfilter].clone() for subfilter in range(self.dcrNumSubfilters)]
for subfilter, newModel in enumerate(newModels):
dcrModels.conditionDcrModel(subfilter, newModel, self.bbox, gain=2.5)
self.assertMaskedImagesAlmostEqual(dcrModels[subfilter], newModel)
def testConditionDcrModelWithChange(self):
"""Verify conditioning when the model changes by a known amount.
This additionally tests that the variance and mask planes do not change.
"""
dcrModels = DcrModel(modelImages=self.makeTestImages())
newModels = [dcrModels[subfilter].clone() for subfilter in range(self.dcrNumSubfilters)]
for model in newModels:
model.image.array[:] *= 3.
for subfilter, newModel in enumerate(newModels):
dcrModels.conditionDcrModel(subfilter, newModel, self.bbox, gain=1.)
refModel = dcrModels[subfilter]
refModel.image.array[:] *= 2.
self.assertMaskedImagesAlmostEqual(refModel, newModel)
def testRegularizationLargeClamp(self):
"""Frequency regularization should leave the models unchanged if the clamp factor is large.
This also tests that noise-like pixels are not regularized.
"""
regularizeSigma = 1.
clampFrequency = 3.
dcrModels = DcrModel(modelImages=self.makeTestImages())
statsCtrl = afwMath.StatisticsControl()
refModels = [dcrModels[subfilter].clone() for subfilter in range(self.dcrNumSubfilters)]
mask = refModels[0].mask
dcrModels.regularizeModel(self.bbox, mask, statsCtrl, regularizeSigma, clampFrequency)
for subfilter, refModel in enumerate(refModels):
self.assertMaskedImagesEqual(dcrModels[subfilter], refModel)
def testRegularizationSmallClamp(self):
"""Test that large variations between model planes are reduced.
This also tests that noise-like pixels are not regularized.
"""
regularizeSigma = 1.
clampFrequency = 1.1
dcrModels = DcrModel(modelImages=self.makeTestImages())
statsCtrl = afwMath.StatisticsControl()
refModels = [dcrModels[subfilter].clone() for subfilter in range(self.dcrNumSubfilters)]
mask = refModels[0].mask
templateImage = dcrModels.getReferenceImage(self.bbox)
dcrModels.regularizeModel(self.bbox, mask, statsCtrl, regularizeSigma, clampFrequency)
for subfilter, refModel in enumerate(refModels):
model = dcrModels[subfilter]
noiseLevel = dcrModels.calculateNoiseCutoff(refModel, statsCtrl, regularizeSigma)
# The mask and variance planes should be unchanged
self.assertFloatsEqual(model.mask.array, refModel.mask.array)
self.assertFloatsEqual(model.variance.array, refModel.variance.array)
# Make sure the test parameters do reduce the outliers
self.assertGreater(np.max(refModel.image.array - templateImage),
np.max(model.image.array - templateImage))
highThreshold = templateImage*clampFrequency + regularizeSigma*noiseLevel
self.assertTrue(np.all(model.image.array <= highThreshold))
lowThreshold = templateImage/clampFrequency - regularizeSigma*noiseLevel
self.assertTrue(np.all(model.image.array >= lowThreshold))
def testModelClamp(self):
"""Test that large amplitude changes between iterations are restricted.
This also tests that noise-like pixels are not regularized.
"""
regularizeSigma = 3.
modelClampFactor = 2.
subfilter = 0
dcrModels = DcrModel(modelImages=self.makeTestImages())
seed = 5
rng = np.random.RandomState(seed)
oldModel = dcrModels[0]
xSize, ySize = self.bbox.getDimensions()
statsCtrl = afwMath.StatisticsControl()
newModel = oldModel.clone()
newModel.image.array[:] += rng.rand(ySize, xSize)*np.max(oldModel.image.array)
newModelRef = newModel.clone()
dcrModels.clampModel(subfilter, newModel, self.bbox, statsCtrl, regularizeSigma, modelClampFactor)
# The mask and variance planes should be unchanged
self.assertFloatsEqual(newModel.mask.array, oldModel.mask.array)
self.assertFloatsEqual(newModel.variance.array, oldModel.variance.array)
# Make sure the test parameters do reduce the outliers
self.assertGreater(np.max(newModelRef.image.array),
np.max(newModel.image.array - oldModel.image.array))
# Check that all of the outliers are clipped
noiseLevel = dcrModels.calculateNoiseCutoff(oldModel, statsCtrl, regularizeSigma)
highThreshold = (oldModel.image.array*modelClampFactor +
noiseLevel*regularizeSigma)
self.assertTrue(np.all(newModel.image.array <= highThreshold))
lowThreshold = oldModel.image.array/modelClampFactor - noiseLevel
self.assertTrue(np.all(newModel.image.array >= lowThreshold))
def testIterateModel(self):
"""Test that the DcrModel is iterable, and has the right values.
"""
testModels = self.makeTestImages()
refVals = [np.sum(model.image.array) for model in testModels]
dcrModels = DcrModel(modelImages=testModels)
for refVal, model in zip(refVals, dcrModels):
self.assertFloatsEqual(refVal, np.sum(model.image.array))
# Negative indices are allowed, so check that those return models from the end.
self.assertFloatsEqual(refVals[-1], np.sum(dcrModels[-1].image.array))
class MyMemoryTestCase(lsst.utils.tests.MemoryTestCase):
pass
def setup_module(module):
lsst.utils.tests.init()
348 ↛ 349line 348 didn't jump to line 349, because the condition on line 348 was never trueif __name__ == "__main__":
lsst.utils.tests.init()
unittest.main()
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