Coverage for tests/test_gaap.py: 12%

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1# This file is part of meas_extensions_gaap 

2# 

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22 

23import math 

24import unittest 

25import galsim 

26import itertools 

27import lsst.afw.detection as afwDetection 

28import lsst.afw.display as afwDisplay 

29import lsst.afw.geom as afwGeom 

30import lsst.afw.image as afwImage 

31import lsst.afw.table as afwTable 

32import lsst.daf.base as dafBase 

33import lsst.geom as geom 

34from lsst.pex.exceptions import InvalidParameterError 

35import lsst.meas.base as measBase 

36import lsst.meas.base.tests 

37import lsst.meas.extensions.gaap 

38import lsst.utils.tests 

39import numpy as np 

40import scipy 

41 

42 

43try: 

44 type(display) 

45except NameError: 

46 display = False 

47 frame = 1 

48 

49 

50def makeGalaxyExposure(scale, psfSigma=0.9, flux=1000., galSigma=3.7, variance=1.0): 

51 """Make an ideal exposure of circular Gaussian 

52 

53 For the purpose of testing Gaussian Aperture and PSF algorithm (GAaP), this 

54 generates a noiseless image of circular Gaussian galaxy of a desired total 

55 flux convolved by a circular Gaussian PSF. The Gaussianity of the galaxy 

56 and the PSF allows comparison with analytical results, modulo pixelization. 

57 

58 Parameters 

59 ---------- 

60 scale : `float` 

61 Pixel scale in the exposure. 

62 psfSigma : `float` 

63 Sigma of the circular Gaussian PSF. 

64 flux : `float` 

65 The total flux of the galaxy. 

66 galSigma : `float` 

67 Sigma of the pre-seeing circular Gaussian galaxy. 

68 

69 Returns 

70 ------- 

71 exposure, center 

72 A tuple containing an lsst.afw.image.Exposure and lsst.geom.Point2D 

73 objects, corresponding to the galaxy image and its centroid. 

74 """ 

75 psfWidth = 2*int(4.0*psfSigma) + 1 

76 galWidth = 2*int(40.*math.hypot(galSigma, psfSigma)) + 1 

77 gal = galsim.Gaussian(sigma=galSigma, flux=flux) 

78 

79 galIm = galsim.Image(galWidth, galWidth) 

80 galIm = galsim.Convolve([gal, galsim.Gaussian(sigma=psfSigma, flux=1.)]).drawImage(image=galIm, 

81 scale=1.0, 

82 method='no_pixel') 

83 exposure = afwImage.makeExposure(afwImage.makeMaskedImageFromArrays(galIm.array)) 

84 exposure.setPsf(afwDetection.GaussianPsf(psfWidth, psfWidth, psfSigma)) 

85 

86 exposure.variance.set(variance) 

87 exposure.mask.set(0) 

88 center = exposure.getBBox().getCenter() 

89 

90 cdMatrix = afwGeom.makeCdMatrix(scale=scale) 

91 exposure.setWcs(afwGeom.makeSkyWcs(crpix=center, 

92 crval=geom.SpherePoint(0.0, 0.0, geom.degrees), 

93 cdMatrix=cdMatrix)) 

94 return exposure, center 

95 

96 

97class GaapFluxTestCase(lsst.meas.base.tests.AlgorithmTestCase, lsst.utils.tests.TestCase): 

98 """Main test case for the GAaP plugin. 

99 """ 

100 def setUp(self): 

101 self.center = lsst.geom.Point2D(100.0, 770.0) 

102 self.bbox = lsst.geom.Box2I(lsst.geom.Point2I(-20, -30), 

103 lsst.geom.Extent2I(240, 1600)) 

104 self.dataset = lsst.meas.base.tests.TestDataset(self.bbox) 

105 

106 # We will consider three sources in our test case 

107 # recordId = 0: A bright point source 

108 # recordId = 1: An elliptical (Gaussian) galaxy 

109 # recordId = 2: A source near a corner 

110 self.dataset.addSource(1000., self.center - lsst.geom.Extent2I(0, 100)) 

111 self.dataset.addSource(1000., self.center + lsst.geom.Extent2I(0, 100), 

112 afwGeom.Quadrupole(9., 9., 4.)) 

113 self.dataset.addSource(600., lsst.geom.Point2D(self.bbox.getMin()) + lsst.geom.Extent2I(10, 10)) 

114 

115 def tearDown(self): 

116 del self.center 

117 del self.bbox 

118 del self.dataset 

119 

120 def makeAlgorithm(self, gaapConfig=None): 

121 schema = lsst.meas.base.tests.TestDataset.makeMinimalSchema() 

122 if gaapConfig is None: 

123 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig() 

124 gaapPlugin = lsst.meas.extensions.gaap.SingleFrameGaapFluxPlugin(gaapConfig, 

125 "ext_gaap_GaapFlux", 

126 schema, None) 

127 if gaapConfig.doOptimalPhotometry: 

128 afwTable.QuadrupoleKey.addFields(schema, "psfShape", "PSF shape") 

129 schema.getAliasMap().set("slot_PsfShape", "psfShape") 

130 return gaapPlugin, schema 

131 

132 def check(self, psfSigma=0.5, flux=1000., scalingFactors=[1.15], forced=False): 

133 """Check for non-negative values for GAaP instFlux and instFluxErr. 

134 """ 

135 scale = 0.1*geom.arcseconds 

136 

137 TaskClass = measBase.ForcedMeasurementTask if forced else measBase.SingleFrameMeasurementTask 

138 

139 # Create an image of a tiny source 

140 exposure, center = makeGalaxyExposure(scale, psfSigma, flux, galSigma=0.001, variance=0.) 

141 

142 measConfig = TaskClass.ConfigClass() 

143 algName = "ext_gaap_GaapFlux" 

144 

145 measConfig.plugins.names.add(algName) 

146 

147 if forced: 

148 measConfig.copyColumns = {"id": "objectId", "parent": "parentObjectId"} 

149 

150 algConfig = measConfig.plugins[algName] 

151 algConfig.scalingFactors = scalingFactors 

152 algConfig.scaleByFwhm = True 

153 algConfig.doPsfPhotometry = True 

154 # Do not turn on optimal photometry; not robust for a point-source. 

155 algConfig.doOptimalPhotometry = False 

156 

157 if forced: 

158 offset = geom.Extent2D(-12.3, 45.6) 

159 refWcs = exposure.getWcs().copyAtShiftedPixelOrigin(offset) 

160 refSchema = afwTable.SourceTable.makeMinimalSchema() 

161 centroidKey = afwTable.Point2DKey.addFields(refSchema, "my_centroid", doc="centroid", 

162 unit="pixel") 

163 shapeKey = afwTable.QuadrupoleKey.addFields(refSchema, "my_shape", "shape") 

164 refSchema.getAliasMap().set("slot_Centroid", "my_centroid") 

165 refSchema.getAliasMap().set("slot_Shape", "my_shape") 

166 refSchema.addField("my_centroid_flag", type="Flag", doc="centroid flag") 

167 refSchema.addField("my_shape_flag", type="Flag", doc="shape flag") 

168 refCat = afwTable.SourceCatalog(refSchema) 

169 refSource = refCat.addNew() 

170 refSource.set(centroidKey, center + offset) 

171 refSource.set(shapeKey, afwGeom.Quadrupole(1.0, 1.0, 0.0)) 

172 

173 refSource.setCoord(refWcs.pixelToSky(refSource.get(centroidKey))) 

174 taskInitArgs = (refSchema,) 

175 taskRunArgs = (refCat, refWcs) 

176 else: 

177 taskInitArgs = (afwTable.SourceTable.makeMinimalSchema(),) 

178 taskRunArgs = () 

179 

180 # Activate undeblended measurement with the same configuration 

181 measConfig.undeblended.names.add(algName) 

182 measConfig.undeblended[algName] = measConfig.plugins[algName] 

183 

184 # We are no longer going to change the configs. 

185 # So validate and freeze as they would happen when run from a CLI 

186 measConfig.validate() 

187 measConfig.freeze() 

188 

189 algMetadata = dafBase.PropertyList() 

190 task = TaskClass(*taskInitArgs, config=measConfig, algMetadata=algMetadata) 

191 

192 schema = task.schema 

193 measCat = afwTable.SourceCatalog(schema) 

194 source = measCat.addNew() 

195 source.getTable().setMetadata(algMetadata) 

196 ss = afwDetection.FootprintSet(exposure.getMaskedImage(), afwDetection.Threshold(10.0)) 

197 fp = ss.getFootprints()[0] 

198 source.setFootprint(fp) 

199 

200 task.run(measCat, exposure, *taskRunArgs) 

201 

202 if display: 

203 disp = afwDisplay.Display(frame) 

204 disp.mtv(exposure) 

205 disp.dot("x", *center, origin=afwImage.PARENT, title="psfSigma=%f" % (psfSigma,)) 

206 

207 self.assertFalse(source.get(algName + "_flag")) # algorithm succeeded 

208 

209 # We first check if it produces a positive number (non-nan) 

210 for baseName in algConfig.getAllGaapResultNames(algName): 

211 self.assertTrue((source.get(baseName + "_instFlux") >= 0)) 

212 self.assertTrue((source.get(baseName + "_instFluxErr") >= 0)) 

213 

214 # For scalingFactor > 1, check if the measured value is close to truth. 

215 for baseName in algConfig.getAllGaapResultNames(algName): 

216 if "_1_0x_" not in baseName: 

217 rtol = 0.1 if "PsfFlux" not in baseName else 0.2 

218 self.assertFloatsAlmostEqual(source.get(baseName + "_instFlux"), flux, rtol=rtol) 

219 

220 def runGaap(self, forced, psfSigma, scalingFactors=(1.0, 1.05, 1.1, 1.15, 1.2, 1.5, 2.0)): 

221 self.check(psfSigma=psfSigma, forced=forced, scalingFactors=scalingFactors) 

222 

223 @lsst.utils.tests.methodParameters(psfSigma=(1.7, 0.95, 1.3,)) 

224 def testGaapPluginUnforced(self, psfSigma): 

225 """Run GAaP as Single-frame measurement plugin. 

226 """ 

227 self.runGaap(False, psfSigma) 

228 

229 @lsst.utils.tests.methodParameters(psfSigma=(1.7, 0.95, 1.3,)) 

230 def testGaapPluginForced(self, psfSigma): 

231 """Run GAaP as forced measurement plugin. 

232 """ 

233 self.runGaap(True, psfSigma) 

234 

235 def testFail(self, scalingFactors=[100.], sigmas=[500.]): 

236 """Test that the fail method sets the flags correctly. 

237 

238 Set config parameters that are guaranteed to raise exceptions, 

239 and check that they are handled properly by the `fail` method and that 

240 expected log messages are generated. 

241 For failure modes not handled by the `fail` method, we test them 

242 in the ``testFlags`` method. 

243 """ 

244 algName = "ext_gaap_GaapFlux" 

245 dependencies = ("base_SdssShape",) 

246 config = self.makeSingleFrameMeasurementConfig(algName, dependencies=dependencies) 

247 gaapConfig = config.plugins[algName] 

248 gaapConfig.scalingFactors = scalingFactors 

249 gaapConfig.sigmas = sigmas 

250 gaapConfig.doPsfPhotometry = True 

251 gaapConfig.doOptimalPhotometry = True 

252 

253 gaapConfig.scaleByFwhm = True 

254 self.assertTrue(gaapConfig.scaleByFwhm) # Test the getter method. 

255 

256 algMetadata = lsst.daf.base.PropertyList() 

257 sfmTask = self.makeSingleFrameMeasurementTask(algName, dependencies=dependencies, config=config, 

258 algMetadata=algMetadata) 

259 exposure, catalog = self.dataset.realize(0.0, sfmTask.schema) 

260 self.recordPsfShape(catalog) 

261 

262 # Expected error messages in the logs when running `sfmTask`. 

263 errorMessage = [("Failed to solve for PSF matching kernel in GAaP for (100.000000, 670.000000): " 

264 "Problematic scaling factors = 100.0 " 

265 "Errors: Exception('Unable to determine kernel sum; 0 candidates')"), 

266 ("Failed to solve for PSF matching kernel in GAaP for (100.000000, 870.000000): " 

267 "Problematic scaling factors = 100.0 " 

268 "Errors: Exception('Unable to determine kernel sum; 0 candidates')"), 

269 ("Failed to solve for PSF matching kernel in GAaP for (-10.000000, -20.000000): " 

270 "Problematic scaling factors = 100.0 " 

271 "Errors: Exception('Unable to determine kernel sum; 0 candidates')")] 

272 

273 plugin_logger_name = sfmTask.log.getChild(algName).name 

274 self.assertEqual(plugin_logger_name, "lsst.measurement.ext_gaap_GaapFlux") 

275 with self.assertLogs(plugin_logger_name, "ERROR") as cm: 

276 sfmTask.run(catalog, exposure) 

277 self.assertEqual([record.message for record in cm.records], errorMessage) 

278 

279 for record in catalog: 

280 self.assertFalse(record[algName + "_flag"]) 

281 for scalingFactor in scalingFactors: 

282 flagName = gaapConfig._getGaapResultName(scalingFactor, "flag_gaussianization", algName) 

283 self.assertTrue(record[flagName]) 

284 for sigma in sigmas + ["Optimal"]: 

285 baseName = gaapConfig._getGaapResultName(scalingFactor, sigma, algName) 

286 self.assertTrue(record[baseName + "_flag"]) 

287 self.assertFalse(record[baseName + "_flag_bigPsf"]) 

288 

289 baseName = gaapConfig._getGaapResultName(scalingFactor, "PsfFlux", algName) 

290 self.assertTrue(record[baseName + "_flag"]) 

291 

292 # Try and "fail" with no PSF. 

293 # Since fatal exceptions are not caught by the measurement framework, 

294 # use a context manager and catch it here. 

295 exposure.setPsf(None) 

296 with self.assertRaises(lsst.meas.base.FatalAlgorithmError): 

297 sfmTask.run(catalog, exposure) 

298 

299 def testFlags(self, sigmas=[0.4, 0.5, 0.7], scalingFactors=[1.15, 1.25, 1.4, 100.]): 

300 """Test that GAaP flags are set properly. 

301 

302 Specifically, we test that 

303 

304 1. for invalid combinations of config parameters, only the 

305 appropriate flags are set and not that the entire measurement itself is 

306 flagged. 

307 2. for sources close to the edge, the edge flags are set. 

308 

309 Parameters 

310 ---------- 

311 sigmas : `list` [`float`], optional 

312 The list of sigmas (in arcseconds) to construct the 

313 `SingleFrameGaapFluxConfig`. 

314 scalingFactors : `list` [`float`], optional 

315 The list of scaling factors to construct the 

316 `SingleFrameGaapFluxConfig`. 

317 

318 Raises 

319 ----- 

320 InvalidParameterError 

321 Raised if none of the config parameters will fail a measurement. 

322 

323 Notes 

324 ----- 

325 Since the seeing in the test dataset is 2 pixels, at least one of the 

326 ``sigmas`` should be smaller than at least twice of one of the 

327 ``scalingFactors`` to avoid the InvalidParameterError exception being 

328 raised. 

329 """ 

330 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas, 

331 scalingFactors=scalingFactors) 

332 gaapConfig.scaleByFwhm = True 

333 gaapConfig.doOptimalPhotometry = True 

334 

335 # Make an instance of GAaP algorithm from a config 

336 algName = "ext_gaap_GaapFlux" 

337 algorithm, schema = self.makeAlgorithm(gaapConfig) 

338 # Make a noiseless exposure and measurements for reference 

339 exposure, catalog = self.dataset.realize(0.0, schema) 

340 # Record the PSF shapes if optimal photometry is performed. 

341 if gaapConfig.doOptimalPhotometry: 

342 self.recordPsfShape(catalog) 

343 

344 record = catalog[0] 

345 algorithm.measure(record, exposure) 

346 seeing = exposure.getPsf().getSigma() 

347 pixelScale = exposure.getWcs().getPixelScale().asArcseconds() 

348 # Measurement must fail (i.e., flag_bigPsf and flag must be set) if 

349 # sigma < scalingFactor * seeing 

350 # Ensure that there is at least one combination of parameters that fail 

351 if not (min(gaapConfig.sigmas)/pixelScale < seeing*max(gaapConfig.scalingFactors)): 

352 raise InvalidParameterError("The config parameters do not trigger a measurement failure. " 

353 "Consider including lower values in ``sigmas`` and/or larger values " 

354 "for ``scalingFactors``") 

355 # Ensure that the measurement is not a complete failure 

356 self.assertFalse(record[algName + "_flag"]) 

357 self.assertFalse(record[algName + "_flag_edge"]) 

358 # Ensure that flag_bigPsf is set if sigma < scalingFactor * seeing 

359 for scalingFactor, sigma in itertools.product(gaapConfig.scalingFactors, gaapConfig.sigmas): 

360 targetSigma = scalingFactor*seeing 

361 baseName = gaapConfig._getGaapResultName(scalingFactor, sigma, algName) 

362 # Give some leeway for the edge case and compare against a small 

363 # negative number instead of zero. 

364 if targetSigma*pixelScale - sigma >= -2e-7: 

365 self.assertTrue(record[baseName+"_flag_bigPsf"], 

366 msg=f"bigPsf flag not set for {scalingFactor=} and {sigma=}", 

367 ) 

368 self.assertTrue(record[baseName+"_flag"], 

369 msg=f"Flag not set for {scalingFactor=} and {sigma=}", 

370 ) 

371 else: 

372 self.assertFalse(record[baseName+"_flag_bigPsf"], 

373 msg=f"bigPsf flag set for {scalingFactor=} and {sigma=}", 

374 ) 

375 self.assertFalse(record[baseName+"_flag"], 

376 msg=f"Flag set for {scalingFactor=} and {sigma=}", 

377 ) 

378 

379 # Ensure that flag_bigPsf is set if OptimalShape is not large enough. 

380 if gaapConfig.doOptimalPhotometry: 

381 aperShape = afwTable.QuadrupoleKey(schema[schema.join(algName, "OptimalShape")]).get(record) 

382 for scalingFactor in gaapConfig.scalingFactors: 

383 targetSigma = scalingFactor*seeing 

384 baseName = gaapConfig._getGaapResultName(scalingFactor, "Optimal", algName) 

385 try: 

386 afwGeom.Quadrupole(aperShape.getParameterVector()-[targetSigma**2, targetSigma**2, 0.0], 

387 normalize=True) 

388 self.assertFalse(record[baseName + "_flag_bigPsf"]) 

389 except InvalidParameterError: 

390 self.assertTrue(record[baseName + "_flag_bigPsf"]) 

391 

392 # Ensure that the edge flag is set for the source at the corner. 

393 record = catalog[2] 

394 algorithm.measure(record, exposure) 

395 self.assertTrue(record[algName + "_flag_edge"]) 

396 self.assertFalse(record[algName + "_flag"]) 

397 

398 def recordPsfShape(self, catalog) -> None: 

399 """Record PSF shapes under the appropriate fields in ``catalog``. 

400 

401 This method must be called after the dataset is realized and a catalog 

402 is returned by the `realize` method. It assumes that the schema is 

403 non-minimal and has "psfShape_xx", "psfShape_yy" and "psfShape_xy" 

404 fields setup 

405 

406 Parameters 

407 ---------- 

408 catalog : `~lsst.afw.table.SourceCatalog` 

409 A source catalog containing records of the simulated sources. 

410 """ 

411 psfShapeKey = afwTable.QuadrupoleKey(catalog.schema["slot_PsfShape"]) 

412 for record in catalog: 

413 record.set(psfShapeKey, self.dataset.psfShape) 

414 

415 @staticmethod 

416 def invertQuadrupole(shape: afwGeom.Quadrupole) -> afwGeom.Quadrupole: 

417 """Compute the Quadrupole object corresponding to the inverse matrix. 

418 

419 If M = [[Q.getIxx(), Q.getIxy()], 

420 [Q.getIxy(), Q.getIyy()]] 

421 

422 for the input quadrupole Q, the returned quadrupole R corresponds to 

423 

424 M^{-1} = [[R.getIxx(), R.getIxy()], 

425 [R.getIxy(), R.getIyy()]]. 

426 """ 

427 invShape = afwGeom.Quadrupole(shape.getIyy(), shape.getIxx(), -shape.getIxy()) 

428 invShape.scale(1./shape.getDeterminantRadius()**2) 

429 return invShape 

430 

431 @lsst.utils.tests.methodParameters(gaussianizationMethod=("auto", "overlap-add", "direct", "fft")) 

432 def testGalaxyPhotometry(self, gaussianizationMethod): 

433 """Test GAaP fluxes for extended sources. 

434 

435 Create and run a SingleFrameMeasurementTask with GAaP plugin and reuse 

436 its outputs as reference for ForcedGaapFluxPlugin. In both cases, 

437 the measured flux is compared with the analytical expectation. 

438 

439 For a Gaussian source with intrinsic shape S and intrinsic aperture W, 

440 the GAaP flux is defined as (Eq. A16 of Kuijken et al. 2015) 

441 :math:`\\frac{F}{2\\pi\\det(S)}\\int\\mathrm{d}x\\exp(-x^T(S^{-1}+W^{-1})x/2)` 

442 :math:`F\\frac{\\det(S^{-1})}{\\det(S^{-1}+W^{-1})}` 

443 """ 

444 algName = "ext_gaap_GaapFlux" 

445 dependencies = ("base_SdssShape",) 

446 sfmConfig = self.makeSingleFrameMeasurementConfig(algName, dependencies=dependencies) 

447 forcedConfig = self.makeForcedMeasurementConfig(algName, dependencies=dependencies) 

448 # Turn on optimal photometry explicitly 

449 sfmConfig.plugins[algName].doOptimalPhotometry = True 

450 forcedConfig.plugins[algName].doOptimalPhotometry = True 

451 sfmConfig.plugins[algName].gaussianizationMethod = gaussianizationMethod 

452 forcedConfig.plugins[algName].gaussianizationMethod = gaussianizationMethod 

453 

454 algMetadata = lsst.daf.base.PropertyList() 

455 sfmTask = self.makeSingleFrameMeasurementTask(config=sfmConfig, algMetadata=algMetadata) 

456 forcedTask = self.makeForcedMeasurementTask(config=forcedConfig, algMetadata=algMetadata, 

457 refSchema=sfmTask.schema) 

458 

459 refExposure, refCatalog = self.dataset.realize(0.0, sfmTask.schema) 

460 self.recordPsfShape(refCatalog) 

461 sfmTask.run(refCatalog, refExposure) 

462 

463 # Check if the measured values match the expectations from 

464 # analytical Gaussian integrals 

465 recordId = 1 # Elliptical Gaussian galaxy 

466 refRecord = refCatalog[recordId] 

467 refWcs = self.dataset.exposure.getWcs() 

468 schema = refRecord.schema 

469 trueFlux = refRecord["truth_instFlux"] 

470 intrinsicShapeVector = afwTable.QuadrupoleKey(schema["truth"]).get(refRecord).getParameterVector() \ 

471 - afwTable.QuadrupoleKey(schema["slot_PsfShape"]).get(refRecord).getParameterVector() 

472 intrinsicShape = afwGeom.Quadrupole(intrinsicShapeVector) 

473 invIntrinsicShape = self.invertQuadrupole(intrinsicShape) 

474 # Assert that the measured fluxes agree with analytical expectations. 

475 for sigma in sfmTask.config.plugins[algName]._sigmas: 

476 if sigma == "Optimal": 

477 aperShape = afwTable.QuadrupoleKey(schema[f"{algName}_OptimalShape"]).get(refRecord) 

478 else: 

479 aperShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0) 

480 aperShape.transformInPlace(refWcs.linearizeSkyToPixel(refRecord.getCentroid(), 

481 geom.arcseconds).getLinear()) 

482 

483 invAperShape = self.invertQuadrupole(aperShape) 

484 analyticalFlux = trueFlux*(invIntrinsicShape.getDeterminantRadius() 

485 / invIntrinsicShape.convolve(invAperShape).getDeterminantRadius())**2 

486 for scalingFactor in sfmTask.config.plugins[algName].scalingFactors: 

487 baseName = sfmTask.plugins[algName].ConfigClass._getGaapResultName(scalingFactor, 

488 sigma, algName) 

489 instFlux = refRecord.get(f"{baseName}_instFlux") 

490 self.assertFloatsAlmostEqual(instFlux, analyticalFlux, rtol=5e-3) 

491 

492 measWcs = self.dataset.makePerturbedWcs(refWcs, randomSeed=15) 

493 measDataset = self.dataset.transform(measWcs) 

494 measExposure, truthCatalog = measDataset.realize(0.0, schema) 

495 measCatalog = forcedTask.generateMeasCat(measExposure, refCatalog, refWcs) 

496 forcedTask.attachTransformedFootprints(measCatalog, refCatalog, measExposure, refWcs) 

497 forcedTask.run(measCatalog, measExposure, refCatalog, refWcs) 

498 

499 fullTransform = afwGeom.makeWcsPairTransform(refWcs, measWcs) 

500 localTransform = afwGeom.linearizeTransform(fullTransform, refRecord.getCentroid()).getLinear() 

501 intrinsicShape.transformInPlace(localTransform) 

502 invIntrinsicShape = self.invertQuadrupole(intrinsicShape) 

503 measRecord = measCatalog[recordId] 

504 

505 # Since measCatalog and refCatalog differ only by WCS, the GAaP flux 

506 # measured through consistent apertures must agree with each other. 

507 for sigma in forcedTask.config.plugins[algName]._sigmas: 

508 if sigma == "Optimal": 

509 aperShape = afwTable.QuadrupoleKey(measRecord.schema[f"{algName}_" 

510 "OptimalShape"]).get(measRecord) 

511 else: 

512 aperShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0) 

513 aperShape.transformInPlace(measWcs.linearizeSkyToPixel(measRecord.getCentroid(), 

514 geom.arcseconds).getLinear()) 

515 

516 invAperShape = self.invertQuadrupole(aperShape) 

517 analyticalFlux = trueFlux*(invIntrinsicShape.getDeterminantRadius() 

518 / invIntrinsicShape.convolve(invAperShape).getDeterminantRadius())**2 

519 for scalingFactor in forcedTask.config.plugins[algName].scalingFactors: 

520 baseName = forcedTask.plugins[algName].ConfigClass._getGaapResultName(scalingFactor, 

521 sigma, algName) 

522 instFlux = measRecord.get(f"{baseName}_instFlux") 

523 # The measurement in the measRecord must be consistent with 

524 # the same in the refRecord in addition to analyticalFlux. 

525 self.assertFloatsAlmostEqual(instFlux, refRecord.get(f"{baseName}_instFlux"), rtol=5e-3) 

526 self.assertFloatsAlmostEqual(instFlux, analyticalFlux, rtol=5e-3) 

527 

528 def getFluxErrScaling(self, kernel, aperShape): 

529 """Returns the value by which the standard error has to be scaled due 

530 to noise correlations. 

531 

532 This is an alternative implementation to the `_getFluxErrScaling` 

533 method of `BaseGaapFluxPlugin`, but is less efficient. 

534 

535 Parameters 

536 ---------- 

537 `kernel` : `~lsst.afw.math.Kernel` 

538 The PSF-Gaussianization kernel. 

539 

540 Returns 

541 ------- 

542 fluxErrScaling : `float` 

543 The factor by which the standard error on GAaP flux must be scaled. 

544 """ 

545 kim = afwImage.ImageD(kernel.getDimensions()) 

546 kernel.computeImage(kim, False) 

547 weight = galsim.Image(np.zeros_like(kim.array)) 

548 aperSigma = aperShape.getDeterminantRadius() 

549 trace = aperShape.getIxx() + aperShape.getIyy() 

550 distortion = galsim.Shear(e1=(aperShape.getIxx()-aperShape.getIyy())/trace, 

551 e2=2*aperShape.getIxy()/trace) 

552 gauss = galsim.Gaussian(sigma=aperSigma, flux=2*np.pi*aperSigma**2).shear(distortion) 

553 weight = gauss.drawImage(image=weight, scale=1.0, method='no_pixel') 

554 kwarr = scipy.signal.convolve2d(weight.array, kim.array, boundary='fill') 

555 fluxErrScaling = np.sqrt(np.sum(kwarr*kwarr)) 

556 fluxErrScaling /= np.sqrt(np.pi*aperSigma**2) 

557 return fluxErrScaling 

558 

559 def testCorrelatedNoiseError(self, sigmas=[0.6, 0.8], scalingFactors=[1.15, 1.2, 1.25, 1.3, 1.4]): 

560 """Test the scaling to standard error due to correlated noise. 

561 

562 The uncertainty estimate on GAaP fluxes is scaled by an amount 

563 determined by the auto-correlation function of the PSF-matching kernel; 

564 see Eqs. A11 & A17 of Kuijken et al. (2015). This test ensures that the 

565 calculation of the scaling factors matches the analytical expression 

566 when the PSF-matching kernel is a Gaussian. 

567 

568 Parameters 

569 ---------- 

570 sigmas : `list` [`float`], optional 

571 A list of effective Gaussian aperture sizes. 

572 scalingFactors : `list` [`float`], optional 

573 A list of factors by which the PSF size must be scaled. 

574 

575 Notes 

576 ----- 

577 This unit test tests internal states of the plugin for accuracy and is 

578 specific to the implementation. It uses private variables as a result 

579 and intentionally breaks encapsulation. 

580 """ 

581 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas, 

582 scalingFactors=scalingFactors) 

583 gaapConfig.scaleByFwhm = True 

584 

585 algorithm, schema = self.makeAlgorithm(gaapConfig) 

586 exposure, catalog = self.dataset.realize(0.0, schema) 

587 wcs = exposure.getWcs() 

588 record = catalog[0] 

589 center = self.center 

590 seeing = exposure.getPsf().computeShape(center).getDeterminantRadius() 

591 for scalingFactor in gaapConfig.scalingFactors: 

592 targetSigma = scalingFactor*seeing 

593 modelPsf = afwDetection.GaussianPsf(algorithm.config._modelPsfDimension, 

594 algorithm.config._modelPsfDimension, 

595 targetSigma) 

596 result = algorithm._gaussianize(exposure, modelPsf, record) 

597 kernel = result.psfMatchingKernel 

598 kernelAcf = algorithm._computeKernelAcf(kernel) 

599 for sigma in gaapConfig.sigmas: 

600 intrinsicShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0) 

601 intrinsicShape.transformInPlace(wcs.linearizeSkyToPixel(center, geom.arcseconds).getLinear()) 

602 aperShape = afwGeom.Quadrupole(intrinsicShape.getParameterVector() 

603 - [targetSigma**2, targetSigma**2, 0.0]) 

604 fluxErrScaling1 = algorithm._getFluxErrScaling(kernelAcf, aperShape) 

605 fluxErrScaling2 = self.getFluxErrScaling(kernel, aperShape) 

606 

607 # The PSF matching kernel is a Gaussian of sigma^2 = (f^2-1)s^2 

608 # where f is the scalingFactor and s is the original seeing. 

609 # The integral of ACF of the kernel times the elliptical 

610 # Gaussian described by aperShape is given below. 

611 sigma /= wcs.getPixelScale().asArcseconds() 

612 analyticalValue = ((sigma**2 - (targetSigma)**2)/(sigma**2-seeing**2))**0.5 

613 self.assertFloatsAlmostEqual(fluxErrScaling1, analyticalValue, rtol=1e-4) 

614 self.assertFloatsAlmostEqual(fluxErrScaling1, fluxErrScaling2, rtol=1e-4) 

615 

616 # Try with an elliptical aperture. This is a proxy for 

617 # optimal aperture, since we do not actually measure anything. 

618 aperShape = afwGeom.Quadrupole(8, 6, 3) 

619 fluxErrScaling1 = algorithm._getFluxErrScaling(kernelAcf, aperShape) 

620 fluxErrScaling2 = self.getFluxErrScaling(kernel, aperShape) 

621 self.assertFloatsAlmostEqual(fluxErrScaling1, fluxErrScaling2, rtol=1e-4) 

622 

623 @lsst.utils.tests.methodParameters(noise=(0.001, 0.01, 0.1)) 

624 def testMonteCarlo(self, noise, recordId=1, sigmas=[0.7, 1.0, 1.25], 

625 scalingFactors=[1.1, 1.15, 1.2, 1.3, 1.4]): 

626 """Test GAaP flux uncertainties. 

627 

628 This test should demonstate that the estimated flux uncertainties agree 

629 with those from Monte Carlo simulations. 

630 

631 Parameters 

632 ---------- 

633 noise : `float` 

634 The RMS value of the Gaussian noise field divided by the total flux 

635 of the source. 

636 recordId : `int`, optional 

637 The source Id in the test dataset to measure. 

638 sigmas : `list` [`float`], optional 

639 The list of sigmas (in pixels) to construct the `GaapFluxConfig`. 

640 scalingFactors : `list` [`float`], optional 

641 The list of scaling factors to construct the `GaapFluxConfig`. 

642 """ 

643 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas, 

644 scalingFactors=scalingFactors) 

645 gaapConfig.scaleByFwhm = True 

646 gaapConfig.doPsfPhotometry = True 

647 gaapConfig.doOptimalPhotometry = True 

648 

649 algorithm, schema = self.makeAlgorithm(gaapConfig) 

650 # Make a noiseless exposure and keep measurement record for reference 

651 exposure, catalog = self.dataset.realize(0.0, schema) 

652 if gaapConfig.doOptimalPhotometry: 

653 self.recordPsfShape(catalog) 

654 recordNoiseless = catalog[recordId] 

655 totalFlux = recordNoiseless["truth_instFlux"] 

656 algorithm.measure(recordNoiseless, exposure) 

657 

658 nSamples = 1024 

659 catalog = afwTable.SourceCatalog(schema) 

660 for repeat in range(nSamples): 

661 exposure, cat = self.dataset.realize(noise*totalFlux, schema, randomSeed=repeat) 

662 if gaapConfig.doOptimalPhotometry: 

663 self.recordPsfShape(cat) 

664 record = cat[recordId] 

665 algorithm.measure(record, exposure) 

666 catalog.append(record) 

667 

668 catalog = catalog.copy(deep=True) 

669 for baseName in gaapConfig.getAllGaapResultNames(): 

670 instFluxKey = schema.join(baseName, "instFlux") 

671 instFluxErrKey = schema.join(baseName, "instFluxErr") 

672 instFluxMean = catalog[instFluxKey].mean() 

673 instFluxErrMean = catalog[instFluxErrKey].mean() 

674 instFluxStdDev = catalog[instFluxKey].std() 

675 

676 # GAaP fluxes are not meant to be total fluxes. 

677 # We compare the mean of the noisy measurements to its 

678 # corresponding noiseless measurement instead of the true value 

679 instFlux = recordNoiseless[instFluxKey] 

680 self.assertFloatsAlmostEqual(instFluxErrMean, instFluxStdDev, rtol=0.02) 

681 self.assertLess(abs(instFluxMean - instFlux), 2.0*instFluxErrMean/nSamples**0.5) 

682 

683 

684class TestMemory(lsst.utils.tests.MemoryTestCase): 

685 pass 

686 

687 

688def setup_module(module, backend="virtualDevice"): 

689 lsst.utils.tests.init() 

690 try: 

691 afwDisplay.setDefaultBackend(backend) 

692 except Exception: 

693 print("Unable to configure display backend: %s" % backend) 

694 

695 

696if __name__ == "__main__": 696 ↛ 697line 696 didn't jump to line 697, because the condition on line 696 was never true

697 import sys 

698 

699 from argparse import ArgumentParser 

700 parser = ArgumentParser() 

701 parser.add_argument('--backend', type=str, default="virtualDevice", 

702 help="The backend to use, e.g. 'ds9'. Be sure to 'setup display_<backend>'") 

703 args = parser.parse_args() 

704 

705 setup_module(sys.modules[__name__], backend=args.backend) 

706 unittest.main()