Coverage for tests/test_zogy.py : 19%

Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
# # LSST Data Management System # Copyright 2016-2017 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/>.
ZogyImagePsfMatchConfig, ZogyImagePsfMatchTask
lsst.utils.tests.init()
"""A test case for the Zogy task. """
self.psf1_sigma = 3.3 # sigma of psf of science image self.psf2_sigma = 2.2 # sigma of psf of template image
self.statsControl = afwMath.StatisticsControl() self.statsControl.setNumSigmaClip(3.) self.statsControl.setNumIter(3) self.statsControl.setAndMask(afwImage.Mask .getPlaneBitMask(["INTRP", "EDGE", "SAT", "CR", "DETECTED", "BAD", "NO_DATA", "DETECTED_NEGATIVE"]))
"""Generate a fake aligned template and science image. """ self.svar = svar # variance of noise in science image self.tvar = tvar # variance of noise in template image
seed = 666 self.im1ex, self.im2ex \ = makeFakeImages(size=(255, 257), svar=self.svar, tvar=self.tvar, psf1=self.psf1_sigma, psf2=self.psf2_sigma, n_sources=10, psf_yvary_factor=varyPsf, seed=seed, verbose=False) # Create an array corresponding to the "expected" subtraction (noise only) np.random.seed(seed) self.expectedSubtraction = np.random.normal(scale=np.sqrt(svar), size=self.im1ex.getDimensions()) self.expectedSubtraction -= np.random.normal(scale=np.sqrt(tvar), size=self.im2ex.getDimensions()) self.expectedVar = np.var(self.expectedSubtraction) self.expectedMean = np.mean(self.expectedSubtraction)
statObj = afwMath.makeStatistics(maskedIm.getVariance(), maskedIm.getMask(), afwMath.MEANCLIP, self.statsControl) mn = statObj.getValue(afwMath.MEANCLIP) return mn
statObj = afwMath.makeStatistics(maskedIm, afwMath.VARIANCECLIP, self.statsControl) var = statObj.getValue(afwMath.VARIANCECLIP) return var
statObj = afwMath.makeStatistics(maskedIm, afwMath.MEANCLIP, self.statsControl) var = statObj.getValue(afwMath.MEANCLIP) return var
del self.im1ex del self.im2ex
"""Tests to compare the two images (diffim's or Scorr's).
See below. Also compare the diffim pixels with the "expected" pixels statistics. Only do the latter if Scorr==False. """ D_F.getMaskedImage().getMask()[:, :] = D_R.getMaskedImage().getMask() varMean_F = self._computeVarianceMean(D_F.getMaskedImage()) varMean_R = self._computeVarianceMean(D_R.getMaskedImage()) pixMean_F = self._computePixelMean(D_F.getMaskedImage()) pixMean_R = self._computePixelMean(D_R.getMaskedImage()) pixVar_F = self._computePixelVariance(D_F.getMaskedImage()) pixVar_R = self._computePixelVariance(D_R.getMaskedImage())
if not Scorr: self.assertFloatsAlmostEqual(varMean_F, varMean_R, rtol=tol) self.assertFloatsAlmostEqual(pixMean_F, self.expectedMean, atol=tol*2.) self.assertFloatsAlmostEqual(pixMean_R, self.expectedMean, atol=tol*2.) self.assertFloatsAlmostEqual(pixVar_F, pixVar_R, rtol=tol) self.assertFloatsAlmostEqual(pixVar_F, self.expectedVar, rtol=tol*2.) self.assertFloatsAlmostEqual(pixVar_R, self.expectedVar, rtol=tol*2.) else: self.assertFloatsAlmostEqual(varMean_F, varMean_R, atol=tol) # nearly zero so need to use atol self.assertFloatsAlmostEqual(pixVar_F, pixVar_R, atol=tol)
self.assertFloatsAlmostEqual(pixMean_F, pixMean_R, atol=tol*2.) # nearly zero so need to use atol
"""Compute Zogy diffims using Fourier- and Real-space methods.
Compare the images. They are not identical but should be similar (within ~2%). """ self._setUpImages() config = ZogyConfig() task = ZogyTask(templateExposure=self.im2ex, scienceExposure=self.im1ex, config=config) D_F = task.computeDiffim(inImageSpace=False) D_R = task.computeDiffim(inImageSpace=True) # Fourier-space and image-space versions are not identical, so up the tolerance. # This is a known issue with the image-space version. self._compareExposures(D_F.D, D_R.D, tol=0.03)
"""Compute Zogy likelihood images (Scorr) using Fourier- and Real-space methods.
Compare the images. They are not identical but should be similar (within ~2%). """ config = ZogyConfig() task = ZogyTask(templateExposure=self.im2ex, scienceExposure=self.im1ex, config=config) D_F = task.computeScorr(inImageSpace=False, xVarAst=varAst, yVarAst=varAst) D_R = task.computeScorr(inImageSpace=True, xVarAst=varAst, yVarAst=varAst) self._compareExposures(D_F.S, D_R.S, Scorr=True)
"""Compute Zogy likelihood images (Scorr) using Fourier- and Real-space methods.
Do the computation with "astrometric variance" both zero and non-zero. Compare the images. They are not identical but should be similar (within ~2%). """ self._setUpImages() self._testZogyScorr() self._testZogyScorr(varAst=0.1)
"""Test running Zogy using ImageMapReduceTask framework.
Compare map-reduced version with non-map-reduced version. Do it for pure Fourier-based calc. and also for real-space. Also for computing pure diffim D and corrected likelihood image Scorr. """ config = ZogyMapReduceConfig() config.gridStepX = config.gridStepY = 9 config.borderSizeX = config.borderSizeY = 3 if inImageSpace: config.gridStepX = config.gridStepY = 8 config.borderSizeX = config.borderSizeY = 6 # need larger border size for image-space run config.reducer.reduceOperation = 'average' task = ImageMapReduceTask(config=config) D_mapReduced = task.run(self.im1ex, template=self.im2ex, inImageSpace=inImageSpace, doScorr=doScorr, forceEvenSized=False, **kwargs).exposure
config = ZogyConfig() task = ZogyTask(templateExposure=self.im2ex, scienceExposure=self.im1ex, config=config) if not doScorr: D = task.computeDiffim(inImageSpace=inImageSpace, **kwargs).D else: D = task.computeScorr(inImageSpace=inImageSpace, **kwargs).S
self._compareExposures(D_mapReduced, D, tol=0.04, Scorr=doScorr)
"""Test running Zogy using ImageMapReduceTask framework.
Compare map-reduced version with non-map-reduced version. Do it for pure Fourier-based calc. and also for real-space. Do it for ZOGY diffim and corrected likelihood image Scorr. For Scorr, do it for zero and non-zero astrometric variance. """ self._setUpImages() self._testZogyDiffimMapReduced(inImageSpace=False) self._testZogyDiffimMapReduced(inImageSpace=True) self._testZogyDiffimMapReduced(inImageSpace=False, doScorr=True) self._testZogyDiffimMapReduced(inImageSpace=True, doScorr=True) self._testZogyDiffimMapReduced(inImageSpace=False, doScorr=True, xVarAst=0.1, yVarAst=0.1) self._testZogyDiffimMapReduced(inImageSpace=True, doScorr=True, xVarAst=0.1, yVarAst=0.1)
doScorr=False, **kwargs): """Test running Zogy using ZogyImagePsfMatchTask framework.
Compare resulting diffim version with original, non-spatially-varying version. """ config = ZogyImagePsfMatchConfig() config.zogyMapReduceConfig.gridStepX = config.zogyMapReduceConfig.gridStepY = 9 config.zogyMapReduceConfig.borderSizeX = config.zogyMapReduceConfig.borderSizeY = 3 if inImageSpace: # need larger border size for image-space run config.zogyMapReduceConfig.gridStepX = config.zogyMapReduceConfig.gridStepY = 8 config.zogyMapReduceConfig.borderSizeX = config.zogyMapReduceConfig.borderSizeY = 6 task = ZogyImagePsfMatchTask(config=config) result = task.subtractExposures(self.im2ex, self.im1ex, inImageSpace=inImageSpace, doWarping=False, spatiallyVarying=spatiallyVarying) D_fromTask = result.subtractedExposure
config = ZogyConfig() task = ZogyTask(templateExposure=self.im2ex, scienceExposure=self.im1ex, config=config) D = task.computeDiffim(inImageSpace=inImageSpace, **kwargs).D self._compareExposures(D_fromTask, D, tol=0.04, Scorr=doScorr)
"""Test running ZogyTask both with and without the spatiallyVarying option. """ self._setUpImages() self._testZogyImagePsfMatchTask(inImageSpace=False) self._testZogyImagePsfMatchTask(inImageSpace=True) self._testZogyImagePsfMatchTask(inImageSpace=False, spatiallyVarying=True) self._testZogyImagePsfMatchTask(inImageSpace=True, spatiallyVarying=True)
"""Test running ZogyTask both with and without the spatiallyVarying option.
Here we artificially set the two images to have PSFs with different dimensions to ensure this edge case passes. This also tests cases where one of the PSFs is not square. """ import lsst.afw.geom as afwGeom import lsst.meas.algorithms as measAlg
# All this to grow the PSF of im1ex by a few pixels: def _growPsf(exp, extraPix=(2, 3)): bbox = exp.getBBox() center = ((bbox.getBeginX() + bbox.getEndX()) // 2., (bbox.getBeginY() + bbox.getEndY()) // 2.) center = afwGeom.Point2D(center[0], center[1]) kern = exp.getPsf().computeKernelImage(center).convertF() kernSize = kern.getDimensions() paddedKern = afwImage.ImageF(kernSize[0] + extraPix[0], kernSize[1] + extraPix[1]) bboxToPlace = afwGeom.Box2I(afwGeom.Point2I((kernSize[0] + extraPix[0] - kern.getWidth()) // 2, (kernSize[1] + extraPix[1] - kern.getHeight()) // 2), kern.getDimensions()) paddedKern.assign(kern, bboxToPlace) fixedKern = afwMath.FixedKernel(paddedKern.convertD()) psfNew = measAlg.KernelPsf(fixedKern, center) exp.setPsf(psfNew) return exp
def _runAllTests(): self._testZogyImagePsfMatchTask(inImageSpace=False) self._testZogyImagePsfMatchTask(inImageSpace=True) self._testZogyImagePsfMatchTask(inImageSpace=False, spatiallyVarying=True) self._testZogyImagePsfMatchTask(inImageSpace=True, spatiallyVarying=True)
# Try a range of PSF size combinations... self._setUpImages() self.im1ex = _growPsf(self.im1ex, (2, 3)) _runAllTests()
self.im2ex = _growPsf(self.im2ex, (3, 2)) _runAllTests()
self._setUpImages() self.im2ex = _growPsf(self.im2ex, (1, 0)) _runAllTests()
self.im2ex = _growPsf(self.im2ex, (3, 6)) _runAllTests()
self.im1ex = _growPsf(self.im1ex, (5, 6)) _runAllTests()
lsst.utils.tests.init() unittest.main() |