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# 

# LSST Data Management System 

# Copyright 2008-2016 AURA/LSST. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program is distributed in the hope that it will be useful, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <https://www.lsstcorp.org/LegalNotices/>. 

# 

 

 

import unittest 

 

 

import lsst.utils.tests 

from lsst.afw.geom import makeSkyWcs 

import lsst.afw.image as afwImage 

import lsst.afw.math as afwMath 

import lsst.ip.diffim as ipDiffim 

import lsst.ip.diffim.diffimTools as diffimTools 

import lsst.daf.base as dafBase 

import lsst.log.utils as logUtils 

import lsst.meas.algorithms as measAlg 

 

logUtils.traceSetAt("ip.diffim", 4) 

 

 

class PsfMatchTestCases(unittest.TestCase): 

 

def setUp(self): 

self.configAL = ipDiffim.ImagePsfMatchTask.ConfigClass() 

self.configAL.kernel.name = "AL" 

self.subconfigAL = self.configAL.kernel.active 

 

self.configDF = ipDiffim.ImagePsfMatchTask.ConfigClass() 

self.configDF.kernel.name = "DF" 

self.subconfigDF = self.configDF.kernel.active 

 

self.configDFr = ipDiffim.ImagePsfMatchTask.ConfigClass() 

self.configDFr.kernel.name = "DF" 

self.subconfigDFr = self.configDFr.kernel.active 

 

self.subconfigAL.afwBackgroundConfig.useApprox = False 

self.subconfigDF.afwBackgroundConfig.useApprox = False 

self.subconfigDFr.afwBackgroundConfig.useApprox = False 

 

self.subconfigDF.useRegularization = False 

self.subconfigDFr.useRegularization = True 

 

self.subconfigAL.constantVarianceWeighting = False 

self.subconfigDF.constantVarianceWeighting = False 

self.subconfigDFr.constantVarianceWeighting = False 

 

# variance is a hack 

self.subconfigAL.singleKernelClipping = False 

self.subconfigAL.spatialKernelClipping = False 

self.subconfigDF.singleKernelClipping = False 

self.subconfigDF.spatialKernelClipping = False 

self.subconfigDFr.singleKernelClipping = False 

self.subconfigDFr.spatialKernelClipping = False 

 

# Send fake kernel a differential background 

self.bgValue = 100. 

self.subconfigAL.fitForBackground = True 

self.subconfigDF.fitForBackground = True 

self.subconfigDFr.fitForBackground = True 

 

# Make ideal PSF 

self.ksize = 21 

self.sigma = 2.0 

self.psf = measAlg.DoubleGaussianPsf(self.ksize, self.ksize, self.sigma) 

 

def makeWcs(self, offset=0): 

# taken from $AFW_DIR/tests/testMakeWcs.py 

metadata = dafBase.PropertySet() 

metadata.set("SIMPLE", "T") 

metadata.set("BITPIX", -32) 

metadata.set("NAXIS", 2) 

metadata.set("NAXIS1", 1024) 

metadata.set("NAXIS2", 1153) 

metadata.set("RADESYS", 'FK5') 

metadata.set("EQUINOX", 2000.) 

metadata.setDouble("CRVAL1", 215.604025685476) 

metadata.setDouble("CRVAL2", 53.1595451514076) 

metadata.setDouble("CRPIX1", 1109.99981456774 + offset) 

metadata.setDouble("CRPIX2", 560.018167811613 + offset) 

metadata.set("CTYPE1", 'RA---SIN') 

metadata.set("CTYPE2", 'DEC--SIN') 

metadata.setDouble("CD1_1", 5.10808596133527E-05) 

metadata.setDouble("CD1_2", 1.85579539217196E-07) 

metadata.setDouble("CD2_2", -5.10281493481982E-05) 

metadata.setDouble("CD2_1", -8.27440751733828E-07) 

return makeSkyWcs(metadata) 

 

def testWarping(self): 

tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue=self.bgValue) 

 

tWcs = self.makeWcs(offset=0) 

sWcs = self.makeWcs(offset=1) 

tExp = afwImage.ExposureF(tMi, tWcs) 

sExp = afwImage.ExposureF(sMi, sWcs) 

 

# Should fail due to registration problem 

psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL) 

try: 

psfMatchAL.subtractExposures(tExp, sExp, doWarping=True) 

except Exception as e: 

print("testWarning failed with %r" % (e,)) 

pass 

else: 

self.fail() 

 

def testSubtractExposures(self): 

# Test all 3 options 

tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue=self.bgValue) 

 

tWcs = self.makeWcs(offset=0) 

sWcs = self.makeWcs(offset=1) 

tExp = afwImage.ExposureF(tMi, tWcs) 

sExp = afwImage.ExposureF(sMi, sWcs) 

sExp.setPsf(self.psf) 

 

psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL) 

psfMatchDF = ipDiffim.ImagePsfMatchTask(config=self.configDF) 

psfMatchDFr = ipDiffim.ImagePsfMatchTask(config=self.configDFr) 

 

self.assertEqual(psfMatchAL.useRegularization, False) 

self.assertEqual(psfMatchDF.useRegularization, False) 

self.assertEqual(psfMatchDFr.useRegularization, True) 

 

resultsAL = psfMatchAL.subtractExposures(tExp, sExp, doWarping=True) 

psfMatchDF.subtractExposures(tExp, sExp, doWarping=True) 

psfMatchDFr.subtractExposures(tExp, sExp, doWarping=True) 

 

self.assertEqual(type(resultsAL.subtractedExposure), afwImage.ExposureF) 

self.assertEqual(type(resultsAL.psfMatchingKernel), afwMath.LinearCombinationKernel) 

self.assertEqual(type(resultsAL.backgroundModel), afwMath.Chebyshev1Function2D) 

self.assertEqual(type(resultsAL.kernelCellSet), afwMath.SpatialCellSet) 

 

def testMatchExposures(self): 

# Only test 1 option 

tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue=self.bgValue) 

 

tWcs = self.makeWcs(offset=0) 

sWcs = self.makeWcs(offset=1) 

tExp = afwImage.ExposureF(tMi, tWcs) 

sExp = afwImage.ExposureF(sMi, sWcs) 

sExp.setPsf(self.psf) 

 

psfMatchAL = ipDiffim.ImagePsfMatchTask(config=self.configAL) 

resultsAL = psfMatchAL.matchExposures(tExp, sExp, 

templateFwhmPix=2.0, scienceFwhmPix=3.0, doWarping=True) 

self.assertEqual(type(resultsAL.matchedExposure), afwImage.ExposureF) 

self.assertEqual(type(resultsAL.psfMatchingKernel), afwMath.LinearCombinationKernel) 

self.assertEqual(type(resultsAL.backgroundModel), afwMath.Chebyshev1Function2D) 

self.assertEqual(type(resultsAL.kernelCellSet), afwMath.SpatialCellSet) 

 

def testPca(self, nTerms=3): 

tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue=self.bgValue) 

 

tWcs = self.makeWcs(offset=0) 

sWcs = self.makeWcs(offset=0) 

tExp = afwImage.ExposureF(tMi, tWcs) 

sExp = afwImage.ExposureF(sMi, sWcs) 

sExp.setPsf(self.psf) 

 

self.subconfigDF.usePcaForSpatialKernel = True 

self.subconfigDF.numPrincipalComponents = nTerms 

 

psfMatchDF = ipDiffim.ImagePsfMatchTask(config=self.configDF) 

candList = psfMatchDF.makeCandidateList(tExp, sExp, self.ksize) 

resultsDF = psfMatchDF.subtractMaskedImages(tMi, sMi, candList) 

 

spatialKernel = resultsDF.psfMatchingKernel 

spatialKernelSolution = spatialKernel.getSpatialParameters() 

self.assertEqual(len(spatialKernelSolution), nTerms) 

 

# First basis has no spatial variation 

for i in range(1, nTerms): 

self.assertEqual(spatialKernelSolution[0][i], 0.) 

 

# All bases have correct number of terms 

sko = self.subconfigDF.spatialKernelOrder 

nSpatialTerms = int(0.5 * (sko + 1) * (sko + 2)) 

for i in range(len(spatialKernelSolution)): 

self.assertEqual(len(spatialKernelSolution[i]), nSpatialTerms) 

 

spatialBg = resultsDF.backgroundModel 

spatialBgSolution = spatialBg.getParameters() 

bgo = self.subconfigDF.spatialBgOrder 

nBgTerms = int(0.5 * (bgo + 1) * (bgo + 2)) 

self.assertEqual(len(spatialBgSolution), nBgTerms) 

 

def testSubtractMaskedImages(self): 

# Lets do some additional testing here to make sure we recover 

# the known spatial model. No background, just the faked 

# alard-lupton basis set. The rest of matchMaskedImages() and 

# subtractMaskedImages() functionality is tested by the 

# Exposure-based methods. 

fakeCoeffs = diffimTools.fakeCoeffs() 

 

# Quick note; you shouldn't change confake here, since the 

# candidates in the KernelCellSet are initialized in 

# makeFakeKernelSet 

tMi, sMi, sK, kcs, confake = diffimTools.makeFakeKernelSet(bgValue=0.0, addNoise=False) 

 

svar = sMi.getVariance() 

svar.set(1.0) 

tvar = tMi.getVariance() 

tvar.set(1.0) 

 

basisList = ipDiffim.makeKernelBasisList(confake.kernel.active) 

psfMatchAL = ipDiffim.ImagePsfMatchTask(config=confake) 

spatialSolution, psfMatchingKernel, backgroundModel = psfMatchAL._solve(kcs, basisList) 

 

fitCoeffs = psfMatchingKernel.getSpatialParameters() 

 

for b in range(len(fakeCoeffs)): 

for s in range(len(fakeCoeffs[b])): 

 

if fakeCoeffs[b][s] == 0.0: 

self.assertAlmostEqual(fitCoeffs[b][s], 0.0) 

else: 

# OUTSTANDING ISSUE - WHY IS THIS ONE TERM OFF!?!? 

if b != 4 and s != 0: 

self.assertAlmostEqual(fitCoeffs[b][s]/fakeCoeffs[b][s], 1.0, 1) 

 

def tearDown(self): 

del self.configAL 

del self.configDF 

del self.configDFr 

del self.psf 

 

 

class TestMemory(lsst.utils.tests.MemoryTestCase): 

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

255 ↛ 256line 255 didn't jump to line 256, because the condition on line 255 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()