Coverage for tests/test_imageDecorrelation.py : 15%

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1#
2# LSST Data Management System
3# Copyright 2016-2017 AURA/LSST.
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5# This product includes software developed by the
6# LSST Project (http://www.lsst.org/).
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21import unittest
23import numpy as np
25import lsst.utils.tests
26import lsst.afw.image as afwImage
27import lsst.afw.geom as afwGeom
28import lsst.afw.math as afwMath
29import lsst.geom as geom
30import lsst.meas.algorithms as measAlg
31import lsst.daf.base as dafBase
33from lsst.ip.diffim.imageDecorrelation import (DecorrelateALKernelTask,
34 DecorrelateALKernelMapReduceConfig,
35 DecorrelateALKernelSpatialConfig,
36 DecorrelateALKernelSpatialTask)
37from lsst.ip.diffim.imageMapReduce import ImageMapReduceTask
39try:
40 type(verbose)
41except NameError:
42 verbose = False
45def setup_module(module):
46 lsst.utils.tests.init()
49def singleGaussian2d(x, y, xc, yc, sigma_x=1., sigma_y=1., theta=0., ampl=1.):
50 """! Generate a 2-d Gaussian, possibly elongated and rotated, on a grid of pixel
51 coordinates given by x,y.
52 @param x,y each a 1-d numpy.array containing x- and y- coordinates for independent variables,
53 for example `np.arange(-16, 15)`.
54 @param xc,yc each a float giving the centroid of the gaussian
55 @param sigma_x,sigma_y each a float giving the sigma of the gaussian
56 @param theta a float giving the rotation of the gaussian (degrees)
57 @param ampl a float giving the amplitude of the gaussian
58 @return a 2-d numpy.array containing the normalized 2-d Gaussian
60 @Note this can be done in `astropy.modeling` but for now we have it explicitly here.
61 """
62 theta = (theta/180.) * np.pi
63 cos_theta2, sin_theta2 = np.cos(theta)**2., np.sin(theta)**2.
64 sigma_x2, sigma_y2 = sigma_x**2., sigma_y**2.
65 a = cos_theta2/(2.*sigma_x2) + sin_theta2/(2.*sigma_y2)
66 b = -(np.sin(2.*theta))/(4.*sigma_x2) + (np.sin(2.*theta))/(4.*sigma_y2)
67 c = sin_theta2/(2.*sigma_x2) + cos_theta2/(2.*sigma_y2)
68 xxc, yyc = x-xc, y-yc
69 out = np.exp(-(a*(xxc**2.) + 2.*b*xxc*yyc + c*(yyc**2.)))
70 out /= out.sum()
71 return out
74def makeFakeImages(size=(256, 256), svar=0.04, tvar=0.04, psf1=3.3, psf2=2.2, offset=None,
75 psf_yvary_factor=0., varSourceChange=1/50., theta1=0., theta2=0.,
76 n_sources=500, seed=66, verbose=False):
77 """Deprecated. Use ``test_zogy : makeFakeImages``. DM-25115: This function is kept for
78 numerical compatibility with existing test cases only. It has x,y axes handling
79 and PSF centering bugs.
81 Parameters
82 ----------
83 size : `tuple` of `int`
84 Image pixel size (x,y). Pixel coordinates are set to
85 (-size[0]//2:size[0]//2, -size[1]//2:size[1]//2)
86 svar, tvar : `float`, optional
87 Per pixel variance of the added noise.
88 psf1, psf2 : `float`, optional
89 std. dev. of (Gaussian) PSFs for the two images in x,y direction. Default is
90 [3.3, 3.3] and [2.2, 2.2] for im1 and im2 respectively.
91 offset : `float`, optional
92 add a constant (pixel) astrometric offset between the two images.
93 psf_yvary_factor : `float`, optional
94 psf_yvary_factor vary the y-width of the PSF across the x-axis of the science image (zero,
95 the default, means no variation)
96 varSourceChange : `float`, optional
97 varSourceChange add this amount of fractional flux to a single source closest to
98 the center of the science image.
99 theta1, theta2: `float`, optional
100 PSF Gaussian rotation angles in degrees.
101 n_sources : `int`, optional
102 The number of sources to add to the images. If zero, no sources are
103 generated just background noise.
104 seed : `int`, optional
105 Random number generator seed.
106 verbose : `bool`, optional
107 Print some actual values.
109 Returns
110 -------
111 im1, im2 : `lsst.afw.image.Exposure`
112 The science and template exposures.
114 Notes
115 -----
116 If ``n_sources > 0`` and ``varSourceChange > 0.`` exactly one source,
117 that is closest to the center, will have different fluxes in the two
118 generated images. The flux on the science image will be higher by
119 ``varSourceChange`` fraction.
121 Having sources near the edges really messes up the
122 fitting (probably because of the convolution). So we make sure no
123 sources are near the edge.
125 Also it seems that having the variable source with a large
126 flux increase also messes up the fitting (seems to lead to
127 overfitting -- perhaps to the source itself). This might be fixed by
128 adding more constant sources.
129 """
130 np.random.seed(seed)
132 psf1 = [3.3, 3.3] if psf1 is None else psf1
133 if not hasattr(psf1, "__len__") and not isinstance(psf1, str):
134 psf1 = [psf1, psf1]
135 psf2 = [2.2, 2.2] if psf2 is None else psf2
136 if not hasattr(psf2, "__len__") and not isinstance(psf2, str):
137 psf2 = [psf2, psf2]
138 offset = [0., 0.] if offset is None else offset # astrometric offset (pixels) between the two images
139 if verbose:
140 print('Science PSF:', psf1, theta1)
141 print('Template PSF:', psf2, theta2)
142 print(np.sqrt(psf1[0]**2 - psf2[0]**2))
143 print('Offset:', offset)
145 xim = np.arange(-size[0]//2, size[0]//2, 1)
146 yim = np.arange(-size[1]//2, size[1]//2, 1)
147 x0im, y0im = np.meshgrid(yim, xim)
148 im1 = np.random.normal(scale=np.sqrt(svar), size=x0im.shape) # variance of science image
149 im2 = np.random.normal(scale=np.sqrt(tvar), size=x0im.shape) # variance of template
151 if n_sources > 0:
152 fluxes = np.random.uniform(50, 30000, n_sources)
153 xposns = np.random.uniform(xim.min()+16, xim.max()-5, n_sources)
154 yposns = np.random.uniform(yim.min()+16, yim.max()-5, n_sources)
156 # Make the source closest to the center of the image the one that increases in flux
157 ind = np.argmin(xposns**2. + yposns**2.)
159 # vary the y-width of psf across x-axis of science image (zero means no variation):
160 psf1_yvary = psf_yvary_factor * (yim.mean() - yposns) / yim.max()
161 if verbose:
162 print('PSF y spatial-variation:', psf1_yvary.min(), psf1_yvary.max())
164 for i in range(n_sources):
165 flux = fluxes[i]
166 tmp = flux * singleGaussian2d(x0im, y0im, xposns[i], yposns[i], psf2[0], psf2[1], theta=theta2)
167 im2 += tmp
168 if i == ind:
169 flux += flux * varSourceChange
170 tmp = flux * singleGaussian2d(x0im, y0im, xposns[i]+offset[0], yposns[i]+offset[1],
171 psf1[0], psf1[1]+psf1_yvary[i], theta=theta1)
172 im1 += tmp
174 im1_psf = singleGaussian2d(x0im, y0im, 0, 0, psf1[0], psf1[1], theta=theta1)
175 im2_psf = singleGaussian2d(x0im, y0im, offset[0], offset[1], psf2[0], psf2[1], theta=theta2)
177 def makeWcs(offset=0):
178 """ Make a fake Wcs
180 Parameters
181 ----------
182 offset : float
183 offset the Wcs by this many pixels.
184 """
185 # taken from $AFW_DIR/tests/testMakeWcs.py
186 metadata = dafBase.PropertySet()
187 metadata.set("SIMPLE", "T")
188 metadata.set("BITPIX", -32)
189 metadata.set("NAXIS", 2)
190 metadata.set("NAXIS1", 1024)
191 metadata.set("NAXIS2", 1153)
192 metadata.set("RADESYS", 'FK5')
193 metadata.set("EQUINOX", 2000.)
194 metadata.setDouble("CRVAL1", 215.604025685476)
195 metadata.setDouble("CRVAL2", 53.1595451514076)
196 metadata.setDouble("CRPIX1", 1109.99981456774 + offset)
197 metadata.setDouble("CRPIX2", 560.018167811613 + offset)
198 metadata.set("CTYPE1", 'RA---SIN')
199 metadata.set("CTYPE2", 'DEC--SIN')
200 metadata.setDouble("CD1_1", 5.10808596133527E-05)
201 metadata.setDouble("CD1_2", 1.85579539217196E-07)
202 metadata.setDouble("CD2_2", -5.10281493481982E-05)
203 metadata.setDouble("CD2_1", -8.27440751733828E-07)
204 return afwGeom.makeSkyWcs(metadata)
206 def makeExposure(imgArray, psfArray, imgVariance):
207 """! Convert an image numpy.array and corresponding PSF numpy.array into an exposure.
209 Add the (constant) variance plane equal to `imgVariance`.
211 @param imgArray 2-d numpy.array containing the image
212 @param psfArray 2-d numpy.array containing the PSF image
213 @param imgVariance variance of input image
214 @return a new exposure containing the image, PSF and desired variance plane
215 """
216 # All this code to convert the template image array/psf array into an exposure.
217 bbox = geom.Box2I(geom.Point2I(0, 0), geom.Point2I(imgArray.shape[1]-1, imgArray.shape[0]-1))
218 im1ex = afwImage.ExposureD(bbox)
219 im1ex.getMaskedImage().getImage().getArray()[:, :] = imgArray
220 im1ex.getMaskedImage().getVariance().getArray()[:, :] = imgVariance
221 psfBox = geom.Box2I(geom.Point2I(-12, -12), geom.Point2I(12, 12)) # a 25x25 pixel psf
222 psf = afwImage.ImageD(psfBox)
223 psfBox.shift(geom.Extent2I(size[0]//2, size[1]//2))
224 im1_psf_sub = psfArray[psfBox.getMinX():psfBox.getMaxX()+1, psfBox.getMinY():psfBox.getMaxY()+1]
225 psf.getArray()[:, :] = im1_psf_sub
226 psfK = afwMath.FixedKernel(psf)
227 psfNew = measAlg.KernelPsf(psfK)
228 im1ex.setPsf(psfNew)
229 wcs = makeWcs()
230 im1ex.setWcs(wcs)
231 return im1ex
233 im1ex = makeExposure(im1, im1_psf, svar) # Science image
234 im2ex = makeExposure(im2, im2_psf, tvar) # Template
236 return im1ex, im2ex
239def estimatePixelCorrelation(B, nDist=40, convEdge=17):
240 """Estimate correlation as a function of pixel distance in the image
241 by sampling pixel pairs.
243 Parameters
244 ----------
245 B : `numpy.ndarray` of N x N `float` elements
246 Noise only image with zero pixel expectation value and identical variance
247 in all pixels. Must have equal dimensions.
248 nDist : `int`, optional
249 Estimated distances goes from 0 to nDist-1.
250 nDist must be smaller than the half dimensions of B.
251 convEdge : `int`, optional
252 Edge width where convolution did not happen.
254 Returns
255 -------
256 S : `numpy.ndarray` of nDist `float` elements
257 Correlation from 0 to nDist-1 pix distance. Pixels are normed by their
258 variance estimation. S[0], the autocorrelation, should be close to 1.
259 """
260 S = np.zeros(nDist, dtype=float)
261 nSample = 10000
262 # Cannot use nDist wide edge, otherwise 2nd pixel can go off the image.
263 # Don't bother with it.
264 A = B/np.sqrt(np.mean(B[convEdge:-convEdge, convEdge:-convEdge]
265 * B[convEdge:-convEdge, convEdge:-convEdge]))
266 lEdge = nDist + convEdge
267 rEdge = B.shape[0] - lEdge
268 for r in range(nDist):
269 ind1 = np.random.randint(lEdge, rEdge, (2, nSample))
270 ind2 = np.copy(ind1)
271 # generate delta x,y in random directions uniformly
272 c_dxy = np.exp(2.j*np.pi*np.random.random(nSample))
273 ind2[0] += np.around(np.real(c_dxy)*r).astype(int)
274 ind2[1] += np.around(np.imag(c_dxy)*r).astype(int)
275 S[r] = np.sum(A[ind1[0], ind1[1]] * A[ind2[0], ind2[1]])/nSample
276 return S
279class DiffimCorrectionTest(lsst.utils.tests.TestCase):
280 """A test case for the diffim image decorrelation algorithm.
281 """
283 def setUp(self):
284 self.psf1_sigma = 3.3 # sigma of psf of science image
285 self.psf2_sigma = 2.2 # sigma of psf of template image
287 self.statsControl = afwMath.StatisticsControl()
288 self.statsControl.setNumSigmaClip(3.)
289 self.statsControl.setNumIter(3)
290 self.statsControl.setAndMask(afwImage.Mask
291 .getPlaneBitMask(["INTRP", "EDGE", "SAT", "CR",
292 "DETECTED", "BAD",
293 "NO_DATA", "DETECTED_NEGATIVE"]))
295 def _setUpImages(self, svar=0.04, tvar=0.04, varyPsf=0.):
296 """Generate a fake aligned template and science image.
297 """
299 self.svar = svar # variance of noise in science image
300 self.tvar = tvar # variance of noise in template image
302 self.im1ex, self.im2ex \
303 = makeFakeImages(svar=self.svar, tvar=self.tvar, psf1=self.psf1_sigma, psf2=self.psf2_sigma,
304 n_sources=50, psf_yvary_factor=varyPsf, verbose=False)
306 def _setUpSourcelessImages(self, svar, tvar):
307 """Generate noise only template and science images.
308 """
310 self.svar = svar # variance of noise in science image
311 self.tvar = tvar # variance of noise in template image
313 self.im1ex, self.im2ex = makeFakeImages(
314 svar=self.svar, tvar=self.tvar, psf1=self.psf1_sigma, psf2=self.psf2_sigma,
315 n_sources=0, seed=22, varSourceChange=0, psf_yvary_factor=0)
317 def _computeVarianceMean(self, maskedIm):
318 statObj = afwMath.makeStatistics(maskedIm.getVariance(),
319 maskedIm.getMask(), afwMath.MEANCLIP,
320 self.statsControl)
321 mn = statObj.getValue(afwMath.MEANCLIP)
322 return mn
324 def _computePixelVariance(self, maskedIm):
325 statObj = afwMath.makeStatistics(maskedIm, afwMath.VARIANCECLIP,
326 self.statsControl)
327 var = statObj.getValue(afwMath.VARIANCECLIP)
328 return var
330 def tearDown(self):
331 del self.im1ex
332 del self.im2ex
334 def _makeAndTestUncorrectedDiffim(self):
335 """Create the (un-decorrelated) diffim, and verify that its variance is too low.
336 """
337 # Create the matching kernel. We used Gaussian PSFs for im1 and im2, so we can compute the "expected"
338 # matching kernel sigma.
339 psf1_sig = self.im1ex.getPsf().computeShape().getDeterminantRadius()
340 psf2_sig = self.im2ex.getPsf().computeShape().getDeterminantRadius()
341 sig_match = np.sqrt((psf1_sig**2. - psf2_sig**2.))
342 # Sanity check - make sure PSFs are correct.
343 self.assertFloatsAlmostEqual(sig_match, np.sqrt((self.psf1_sigma**2. - self.psf2_sigma**2.)),
344 rtol=2e-5)
345 # mKernel = measAlg.SingleGaussianPsf(31, 31, sig_match)
346 x0 = np.arange(-16, 16, 1)
347 y0 = x0.copy()
348 x0im, y0im = np.meshgrid(x0, y0)
349 matchingKernel = singleGaussian2d(x0im, y0im, -1., -1., sigma_x=sig_match, sigma_y=sig_match)
350 kernelImg = afwImage.ImageD(matchingKernel.shape[0], matchingKernel.shape[1])
351 kernelImg.getArray()[:, :] = matchingKernel
352 mKernel = afwMath.FixedKernel(kernelImg)
354 # Create the matched template by convolving the template with the matchingKernel
355 matched_im2ex = self.im2ex.clone()
356 convCntrl = afwMath.ConvolutionControl(False, True, 0)
357 afwMath.convolve(matched_im2ex.getMaskedImage(), self.im2ex.getMaskedImage(), mKernel, convCntrl)
359 # Expected (ideal) variance of difference image
360 expected_var = self.svar + self.tvar
361 if verbose:
362 print('EXPECTED VARIANCE:', expected_var)
364 # Create the diffim (uncorrected)
365 # Uncorrected diffim exposure - variance plane is wrong (too low)
366 tmp_diffExp = self.im1ex.getMaskedImage().clone()
367 tmp_diffExp -= matched_im2ex.getMaskedImage()
368 var = self._computeVarianceMean(tmp_diffExp)
369 self.assertLess(var, expected_var)
371 # Uncorrected diffim exposure - variance is wrong (too low) - same as above but on pixels
372 diffExp = self.im1ex.clone()
373 tmp = diffExp.getMaskedImage()
374 tmp -= matched_im2ex.getMaskedImage()
375 var = self._computePixelVariance(diffExp.getMaskedImage())
376 self.assertLess(var, expected_var)
378 # Uncorrected diffim exposure - variance plane is wrong (too low)
379 mn = self._computeVarianceMean(diffExp.getMaskedImage())
380 self.assertLess(mn, expected_var)
381 if verbose:
382 print('UNCORRECTED VARIANCE:', var, mn)
384 return diffExp, mKernel, expected_var
386 def _runDecorrelationTask(self, diffExp, mKernel):
387 """ Run the decorrelation task on the given diffim with the given matching kernel
388 """
389 task = DecorrelateALKernelTask()
390 decorrResult = task.run(self.im1ex, self.im2ex, diffExp, mKernel)
391 corrected_diffExp = decorrResult.correctedExposure
392 return corrected_diffExp
394 def _testDecorrelation(self, expected_var, corrected_diffExp):
395 """ Check that the variance of the corrected diffim matches the theoretical value.
396 """
397 # Corrected diffim - variance should be close to expected.
398 # We set the tolerance a bit higher here since the simulated images have many bright stars
399 var = self._computePixelVariance(corrected_diffExp.getMaskedImage())
400 self.assertFloatsAlmostEqual(var, expected_var, rtol=0.05)
402 # Check statistics of variance plane in corrected diffim
403 mn = self._computeVarianceMean(corrected_diffExp.getMaskedImage())
404 if verbose:
405 print('CORRECTED VARIANCE:', var, mn)
406 self.assertFloatsAlmostEqual(mn, expected_var, rtol=0.02)
407 self.assertFloatsAlmostEqual(var, mn, rtol=0.05)
408 return var, mn
410 def _testDiffimCorrection(self, svar, tvar):
411 """ Run decorrelation and check the variance of the corrected diffim.
412 """
413 self._setUpImages(svar=svar, tvar=tvar)
414 diffExp, mKernel, expected_var = self._makeAndTestUncorrectedDiffim()
415 corrected_diffExp = self._runDecorrelationTask(diffExp, mKernel)
416 self._testDecorrelation(expected_var, corrected_diffExp)
418 def testDiffimCorrection(self):
419 """Test decorrelated diffim from images with different combinations of variances.
420 """
421 # Same variance
422 self._testDiffimCorrection(svar=0.04, tvar=0.04)
423 # Science image variance is higher than that of the template.
424 self._testDiffimCorrection(svar=0.08, tvar=0.04)
425 # Template variance is higher than that of the science img.
426 self._testDiffimCorrection(svar=0.04, tvar=0.08)
428 def testNoiseDiffimCorrection(self):
429 """Test correction by estimating correlation directly on a noise difference image.
431 Notes
432 ------
434 See `lsst-dm/diffimTests` notebook `DM-24371_correlation_estimate.ipynb`
435 for further details of how the correlation looks like in the uncorrected
436 and corrected cases and where the tolerance numbers come from.
437 """
438 svar = 1.
439 tvar = 100.
440 # Based on DM-24371_correlation_estimate.ipynb
441 someCorrelationThreshold = 0.2
443 self._setUpSourcelessImages(svar=svar, tvar=tvar)
444 diffExp, mKernel, expected_var = self._makeAndTestUncorrectedDiffim()
445 corrected_diffExp = self._runDecorrelationTask(diffExp, mKernel)
447 rho_sci = estimatePixelCorrelation(self.im1ex.getImage().getArray())
448 rho_rawdiff = estimatePixelCorrelation(diffExp.getImage().getArray())
449 rho_corrdiff = estimatePixelCorrelation(corrected_diffExp.getImage().getArray())
451 # Autocorrelation sanity check
452 self.assertFloatsAlmostEqual(rho_sci[0], 1., atol=0.1, rtol=None)
453 self.assertFloatsAlmostEqual(rho_rawdiff[0], 1., atol=0.1, rtol=None)
454 self.assertFloatsAlmostEqual(rho_corrdiff[0], 1., atol=0.1, rtol=None)
456 # Uncorrelated input check
457 self.assertFloatsAlmostEqual(rho_sci[1:], 0., atol=0.1, rtol=None)
459 # Without correction there should be correlation up to a few pixel distance
460 self.assertGreater(rho_rawdiff[1], someCorrelationThreshold)
461 self.assertGreater(rho_rawdiff[2], someCorrelationThreshold)
462 self.assertGreater(rho_rawdiff[3], someCorrelationThreshold)
464 # Uncorrelated corrected image check
465 self.assertFloatsAlmostEqual(rho_corrdiff[1:], 0., atol=0.1, rtol=None)
467 def _runDecorrelationTaskMapReduced(self, diffExp, mKernel):
468 """ Run decorrelation using the imageMapReducer.
469 """
470 config = DecorrelateALKernelMapReduceConfig()
471 config.borderSizeX = config.borderSizeY = 3
472 config.reducer.reduceOperation = 'average'
473 task = ImageMapReduceTask(config=config)
474 decorrResult = task.run(diffExp, template=self.im2ex, science=self.im1ex,
475 psfMatchingKernel=mKernel, forceEvenSized=True)
476 corrected_diffExp = decorrResult.exposure
477 return corrected_diffExp
479 def _testDiffimCorrection_mapReduced(self, svar, tvar, varyPsf=0.0):
480 """ Run decorrelation using the imageMapReduce task, and check the variance of
481 the corrected diffim.
482 """
483 self._setUpImages(svar=svar, tvar=tvar, varyPsf=varyPsf)
484 diffExp, mKernel, expected_var = self._makeAndTestUncorrectedDiffim()
485 corrected_diffExp = self._runDecorrelationTaskMapReduced(diffExp, mKernel)
486 self._testDecorrelation(expected_var, corrected_diffExp)
487 # Also compare the diffim generated here vs. the non-ImageMapReduce one
488 corrected_diffExp_OLD = self._runDecorrelationTask(diffExp, mKernel)
489 self.assertMaskedImagesAlmostEqual(corrected_diffExp.getMaskedImage(),
490 corrected_diffExp_OLD.getMaskedImage())
492 @unittest.skip("DM-21868 ImageMapReduce usage is not yet supported")
493 def testDiffimCorrection_mapReduced(self):
494 """ Test decorrelated diffim when using the imageMapReduce task.
495 Compare results with those from the original DecorrelateALKernelTask.
496 """
497 # Same variance
498 self._testDiffimCorrection_mapReduced(svar=0.04, tvar=0.04)
499 # Science image variance is higher than that of the template.
500 self._testDiffimCorrection_mapReduced(svar=0.04, tvar=0.08)
501 # Template variance is higher than that of the science img.
502 self._testDiffimCorrection_mapReduced(svar=0.08, tvar=0.04)
504 def _runDecorrelationSpatialTask(self, diffExp, mKernel, spatiallyVarying=False):
505 """ Run decorrelation using the DecorrelateALKernelSpatialTask.
506 """
507 config = DecorrelateALKernelSpatialConfig()
508 task = DecorrelateALKernelSpatialTask(config=config)
509 decorrResult = task.run(scienceExposure=self.im1ex, templateExposure=self.im2ex,
510 subtractedExposure=diffExp, psfMatchingKernel=mKernel,
511 spatiallyVarying=spatiallyVarying)
512 corrected_diffExp = decorrResult.correctedExposure
513 return corrected_diffExp
515 def _testDiffimCorrection_spatialTask(self, svar, tvar, varyPsf=0.0):
516 """Run decorrelation using the DecorrelateALKernelSpatialTask, and
517 check the variance of the corrected diffim. Do it for `spatiallyVarying` both
518 True and False. Also compare the variances between the two `spatiallyVarying`
519 cases.
520 """
521 self._setUpImages(svar=svar, tvar=tvar, varyPsf=varyPsf)
522 diffExp, mKernel, expected_var = self._makeAndTestUncorrectedDiffim()
523 variances = []
524 for spatiallyVarying in [False, True]:
525 corrected_diffExp = self._runDecorrelationSpatialTask(diffExp, mKernel,
526 spatiallyVarying)
527 var, mn = self._testDecorrelation(expected_var, corrected_diffExp)
528 variances.append(var)
529 self.assertFloatsAlmostEqual(variances[0], variances[1], rtol=0.03)
531 def testDiffimCorrection_spatialTask(self):
532 """Test decorrelated diffim when using the DecorrelateALKernelSpatialTask.
533 Compare results with those from the original DecorrelateALKernelTask.
534 """
535 # Same variance
536 self._testDiffimCorrection_spatialTask(svar=0.04, tvar=0.04)
537 # Science image variance is higher than that of the template.
538 self._testDiffimCorrection_spatialTask(svar=0.04, tvar=0.08)
539 # Template variance is higher than that of the science img.
540 self._testDiffimCorrection_spatialTask(svar=0.08, tvar=0.04)
543class MemoryTester(lsst.utils.tests.MemoryTestCase):
544 pass
547if __name__ == "__main__": 547 ↛ 548line 547 didn't jump to line 548, because the condition on line 547 was never true
548 lsst.utils.tests.init()
549 unittest.main()