Coverage for tests/test_gaap.py: 12%
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1# This file is part of meas_extensions_gaap
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6# See the COPYRIGHT file at the top-level directory of this distribution
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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
43try:
44 type(display)
45except NameError:
46 display = False
47 frame = 1
50def makeGalaxyExposure(scale, psfSigma=0.9, flux=1000., galSigma=3.7, variance=1.0):
51 """Make an ideal exposure of circular Gaussian
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.
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.
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)
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))
86 exposure.variance.set(variance)
87 exposure.mask.set(0)
88 center = exposure.getBBox().getCenter()
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
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)
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))
115 def tearDown(self):
116 del self.center
117 del self.bbox
118 del self.dataset
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
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
137 TaskClass = measBase.ForcedMeasurementTask if forced else measBase.SingleFrameMeasurementTask
139 # Create an image of a tiny source
140 exposure, center = makeGalaxyExposure(scale, psfSigma, flux, galSigma=0.001, variance=0.)
142 measConfig = TaskClass.ConfigClass()
143 algName = "ext_gaap_GaapFlux"
145 measConfig.plugins.names.add(algName)
147 if forced:
148 measConfig.copyColumns = {"id": "objectId", "parent": "parentObjectId"}
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
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))
173 refSource.setCoord(refWcs.pixelToSky(refSource.get(centroidKey)))
174 taskInitArgs = (refSchema,)
175 taskRunArgs = (refCat, refWcs)
176 else:
177 taskInitArgs = (afwTable.SourceTable.makeMinimalSchema(),)
178 taskRunArgs = ()
180 # Activate undeblended measurement with the same configuration
181 measConfig.undeblended.names.add(algName)
182 measConfig.undeblended[algName] = measConfig.plugins[algName]
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()
189 algMetadata = dafBase.PropertyList()
190 task = TaskClass(*taskInitArgs, config=measConfig, algMetadata=algMetadata)
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)
200 task.run(measCat, exposure, *taskRunArgs)
202 if display:
203 disp = afwDisplay.Display(frame)
204 disp.mtv(exposure)
205 disp.dot("x", *center, origin=afwImage.PARENT, title="psfSigma=%f" % (psfSigma,))
207 self.assertFalse(source.get(algName + "_flag")) # algorithm succeeded
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))
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)
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)
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)
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)
235 def testFail(self, scalingFactors=[100.], sigmas=[500.]):
236 """Test that the fail method sets the flags correctly.
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
253 gaapConfig.scaleByFwhm = True
254 self.assertTrue(gaapConfig.scaleByFwhm) # Test the getter method.
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)
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')")]
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)
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"])
289 baseName = gaapConfig._getGaapResultName(scalingFactor, "PsfFlux", algName)
290 self.assertTrue(record[baseName + "_flag"])
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)
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.
302 Specifically, we test that
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.
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`.
318 Raises
319 -----
320 InvalidParameterError
321 Raised if none of the config parameters will fail a measurement.
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
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)
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.
363 if targetSigma - sigma/pixelScale >= -1e-10:
364 self.assertTrue(record[baseName+"_flag_bigPsf"])
365 self.assertTrue(record[baseName+"_flag"])
366 else:
367 self.assertFalse(record[baseName+"_flag_bigPsf"])
368 self.assertFalse(record[baseName+"_flag"])
370 # Ensure that flag_bigPsf is set if OptimalShape is not large enough.
371 if gaapConfig.doOptimalPhotometry:
372 aperShape = afwTable.QuadrupoleKey(schema[schema.join(algName, "OptimalShape")]).get(record)
373 for scalingFactor in gaapConfig.scalingFactors:
374 targetSigma = scalingFactor*seeing
375 baseName = gaapConfig._getGaapResultName(scalingFactor, "Optimal", algName)
376 try:
377 afwGeom.Quadrupole(aperShape.getParameterVector()-[targetSigma**2, targetSigma**2, 0.0],
378 normalize=True)
379 self.assertFalse(record[baseName + "_flag_bigPsf"])
380 except InvalidParameterError:
381 self.assertTrue(record[baseName + "_flag_bigPsf"])
383 # Ensure that the edge flag is set for the source at the corner.
384 record = catalog[2]
385 algorithm.measure(record, exposure)
386 self.assertTrue(record[algName + "_flag_edge"])
387 self.assertFalse(record[algName + "_flag"])
389 def recordPsfShape(self, catalog) -> None:
390 """Record PSF shapes under the appropriate fields in ``catalog``.
392 This method must be called after the dataset is realized and a catalog
393 is returned by the `realize` method. It assumes that the schema is
394 non-minimal and has "psfShape_xx", "psfShape_yy" and "psfShape_xy"
395 fields setup
397 Parameters
398 ----------
399 catalog : `~lsst.afw.table.SourceCatalog`
400 A source catalog containing records of the simulated sources.
401 """
402 psfShapeKey = afwTable.QuadrupoleKey(catalog.schema["slot_PsfShape"])
403 for record in catalog:
404 record.set(psfShapeKey, self.dataset.psfShape)
406 @staticmethod
407 def invertQuadrupole(shape: afwGeom.Quadrupole) -> afwGeom.Quadrupole:
408 """Compute the Quadrupole object corresponding to the inverse matrix.
410 If M = [[Q.getIxx(), Q.getIxy()],
411 [Q.getIxy(), Q.getIyy()]]
413 for the input quadrupole Q, the returned quadrupole R corresponds to
415 M^{-1} = [[R.getIxx(), R.getIxy()],
416 [R.getIxy(), R.getIyy()]].
417 """
418 invShape = afwGeom.Quadrupole(shape.getIyy(), shape.getIxx(), -shape.getIxy())
419 invShape.scale(1./shape.getDeterminantRadius()**2)
420 return invShape
422 @lsst.utils.tests.methodParameters(gaussianizationMethod=("auto", "overlap-add", "direct", "fft"))
423 def testGalaxyPhotometry(self, gaussianizationMethod):
424 """Test GAaP fluxes for extended sources.
426 Create and run a SingleFrameMeasurementTask with GAaP plugin and reuse
427 its outputs as reference for ForcedGaapFluxPlugin. In both cases,
428 the measured flux is compared with the analytical expectation.
430 For a Gaussian source with intrinsic shape S and intrinsic aperture W,
431 the GAaP flux is defined as (Eq. A16 of Kuijken et al. 2015)
432 :math:`\\frac{F}{2\\pi\\det(S)}\\int\\mathrm{d}x\\exp(-x^T(S^{-1}+W^{-1})x/2)`
433 :math:`F\\frac{\\det(S^{-1})}{\\det(S^{-1}+W^{-1})}`
434 """
435 algName = "ext_gaap_GaapFlux"
436 dependencies = ("base_SdssShape",)
437 sfmConfig = self.makeSingleFrameMeasurementConfig(algName, dependencies=dependencies)
438 forcedConfig = self.makeForcedMeasurementConfig(algName, dependencies=dependencies)
439 # Turn on optimal photometry explicitly
440 sfmConfig.plugins[algName].doOptimalPhotometry = True
441 forcedConfig.plugins[algName].doOptimalPhotometry = True
442 sfmConfig.plugins[algName].gaussianizationMethod = gaussianizationMethod
443 forcedConfig.plugins[algName].gaussianizationMethod = gaussianizationMethod
445 algMetadata = lsst.daf.base.PropertyList()
446 sfmTask = self.makeSingleFrameMeasurementTask(config=sfmConfig, algMetadata=algMetadata)
447 forcedTask = self.makeForcedMeasurementTask(config=forcedConfig, algMetadata=algMetadata,
448 refSchema=sfmTask.schema)
450 refExposure, refCatalog = self.dataset.realize(0.0, sfmTask.schema)
451 self.recordPsfShape(refCatalog)
452 sfmTask.run(refCatalog, refExposure)
454 # Check if the measured values match the expectations from
455 # analytical Gaussian integrals
456 recordId = 1 # Elliptical Gaussian galaxy
457 refRecord = refCatalog[recordId]
458 refWcs = self.dataset.exposure.getWcs()
459 schema = refRecord.schema
460 trueFlux = refRecord["truth_instFlux"]
461 intrinsicShapeVector = afwTable.QuadrupoleKey(schema["truth"]).get(refRecord).getParameterVector() \
462 - afwTable.QuadrupoleKey(schema["slot_PsfShape"]).get(refRecord).getParameterVector()
463 intrinsicShape = afwGeom.Quadrupole(intrinsicShapeVector)
464 invIntrinsicShape = self.invertQuadrupole(intrinsicShape)
465 # Assert that the measured fluxes agree with analytical expectations.
466 for sigma in sfmTask.config.plugins[algName]._sigmas:
467 if sigma == "Optimal":
468 aperShape = afwTable.QuadrupoleKey(schema[f"{algName}_OptimalShape"]).get(refRecord)
469 else:
470 aperShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
471 aperShape.transformInPlace(refWcs.linearizeSkyToPixel(refRecord.getCentroid(),
472 geom.arcseconds).getLinear())
474 invAperShape = self.invertQuadrupole(aperShape)
475 analyticalFlux = trueFlux*(invIntrinsicShape.getDeterminantRadius()
476 / invIntrinsicShape.convolve(invAperShape).getDeterminantRadius())**2
477 for scalingFactor in sfmTask.config.plugins[algName].scalingFactors:
478 baseName = sfmTask.plugins[algName].ConfigClass._getGaapResultName(scalingFactor,
479 sigma, algName)
480 instFlux = refRecord.get(f"{baseName}_instFlux")
481 self.assertFloatsAlmostEqual(instFlux, analyticalFlux, rtol=5e-3)
483 measWcs = self.dataset.makePerturbedWcs(refWcs, randomSeed=15)
484 measDataset = self.dataset.transform(measWcs)
485 measExposure, truthCatalog = measDataset.realize(0.0, schema)
486 measCatalog = forcedTask.generateMeasCat(measExposure, refCatalog, refWcs)
487 forcedTask.attachTransformedFootprints(measCatalog, refCatalog, measExposure, refWcs)
488 forcedTask.run(measCatalog, measExposure, refCatalog, refWcs)
490 fullTransform = afwGeom.makeWcsPairTransform(refWcs, measWcs)
491 localTransform = afwGeom.linearizeTransform(fullTransform, refRecord.getCentroid()).getLinear()
492 intrinsicShape.transformInPlace(localTransform)
493 invIntrinsicShape = self.invertQuadrupole(intrinsicShape)
494 measRecord = measCatalog[recordId]
496 # Since measCatalog and refCatalog differ only by WCS, the GAaP flux
497 # measured through consistent apertures must agree with each other.
498 for sigma in forcedTask.config.plugins[algName]._sigmas:
499 if sigma == "Optimal":
500 aperShape = afwTable.QuadrupoleKey(measRecord.schema[f"{algName}_"
501 "OptimalShape"]).get(measRecord)
502 else:
503 aperShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
504 aperShape.transformInPlace(measWcs.linearizeSkyToPixel(measRecord.getCentroid(),
505 geom.arcseconds).getLinear())
507 invAperShape = self.invertQuadrupole(aperShape)
508 analyticalFlux = trueFlux*(invIntrinsicShape.getDeterminantRadius()
509 / invIntrinsicShape.convolve(invAperShape).getDeterminantRadius())**2
510 for scalingFactor in forcedTask.config.plugins[algName].scalingFactors:
511 baseName = forcedTask.plugins[algName].ConfigClass._getGaapResultName(scalingFactor,
512 sigma, algName)
513 instFlux = measRecord.get(f"{baseName}_instFlux")
514 # The measurement in the measRecord must be consistent with
515 # the same in the refRecord in addition to analyticalFlux.
516 self.assertFloatsAlmostEqual(instFlux, refRecord.get(f"{baseName}_instFlux"), rtol=5e-3)
517 self.assertFloatsAlmostEqual(instFlux, analyticalFlux, rtol=5e-3)
519 def getFluxErrScaling(self, kernel, aperShape):
520 """Returns the value by which the standard error has to be scaled due
521 to noise correlations.
523 This is an alternative implementation to the `_getFluxErrScaling`
524 method of `BaseGaapFluxPlugin`, but is less efficient.
526 Parameters
527 ----------
528 `kernel` : `~lsst.afw.math.Kernel`
529 The PSF-Gaussianization kernel.
531 Returns
532 -------
533 fluxErrScaling : `float`
534 The factor by which the standard error on GAaP flux must be scaled.
535 """
536 kim = afwImage.ImageD(kernel.getDimensions())
537 kernel.computeImage(kim, False)
538 weight = galsim.Image(np.zeros_like(kim.array))
539 aperSigma = aperShape.getDeterminantRadius()
540 trace = aperShape.getIxx() + aperShape.getIyy()
541 distortion = galsim.Shear(e1=(aperShape.getIxx()-aperShape.getIyy())/trace,
542 e2=2*aperShape.getIxy()/trace)
543 gauss = galsim.Gaussian(sigma=aperSigma, flux=2*np.pi*aperSigma**2).shear(distortion)
544 weight = gauss.drawImage(image=weight, scale=1.0, method='no_pixel')
545 kwarr = scipy.signal.convolve2d(weight.array, kim.array, boundary='fill')
546 fluxErrScaling = np.sqrt(np.sum(kwarr*kwarr))
547 fluxErrScaling /= np.sqrt(np.pi*aperSigma**2)
548 return fluxErrScaling
550 def testCorrelatedNoiseError(self, sigmas=[0.6, 0.8], scalingFactors=[1.15, 1.2, 1.25, 1.3, 1.4]):
551 """Test the scaling to standard error due to correlated noise.
553 The uncertainty estimate on GAaP fluxes is scaled by an amount
554 determined by the auto-correlation function of the PSF-matching kernel;
555 see Eqs. A11 & A17 of Kuijken et al. (2015). This test ensures that the
556 calculation of the scaling factors matches the analytical expression
557 when the PSF-matching kernel is a Gaussian.
559 Parameters
560 ----------
561 sigmas : `list` [`float`], optional
562 A list of effective Gaussian aperture sizes.
563 scalingFactors : `list` [`float`], optional
564 A list of factors by which the PSF size must be scaled.
566 Notes
567 -----
568 This unit test tests internal states of the plugin for accuracy and is
569 specific to the implementation. It uses private variables as a result
570 and intentionally breaks encapsulation.
571 """
572 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas,
573 scalingFactors=scalingFactors)
574 gaapConfig.scaleByFwhm = True
576 algorithm, schema = self.makeAlgorithm(gaapConfig)
577 exposure, catalog = self.dataset.realize(0.0, schema)
578 wcs = exposure.getWcs()
579 record = catalog[0]
580 center = self.center
581 seeing = exposure.getPsf().computeShape(center).getDeterminantRadius()
582 for scalingFactor in gaapConfig.scalingFactors:
583 targetSigma = scalingFactor*seeing
584 modelPsf = afwDetection.GaussianPsf(algorithm.config._modelPsfDimension,
585 algorithm.config._modelPsfDimension,
586 targetSigma)
587 result = algorithm._gaussianize(exposure, modelPsf, record)
588 kernel = result.psfMatchingKernel
589 kernelAcf = algorithm._computeKernelAcf(kernel)
590 for sigma in gaapConfig.sigmas:
591 intrinsicShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
592 intrinsicShape.transformInPlace(wcs.linearizeSkyToPixel(center, geom.arcseconds).getLinear())
593 aperShape = afwGeom.Quadrupole(intrinsicShape.getParameterVector()
594 - [targetSigma**2, targetSigma**2, 0.0])
595 fluxErrScaling1 = algorithm._getFluxErrScaling(kernelAcf, aperShape)
596 fluxErrScaling2 = self.getFluxErrScaling(kernel, aperShape)
598 # The PSF matching kernel is a Gaussian of sigma^2 = (f^2-1)s^2
599 # where f is the scalingFactor and s is the original seeing.
600 # The integral of ACF of the kernel times the elliptical
601 # Gaussian described by aperShape is given below.
602 sigma /= wcs.getPixelScale().asArcseconds()
603 analyticalValue = ((sigma**2 - (targetSigma)**2)/(sigma**2-seeing**2))**0.5
604 self.assertFloatsAlmostEqual(fluxErrScaling1, analyticalValue, rtol=1e-4)
605 self.assertFloatsAlmostEqual(fluxErrScaling1, fluxErrScaling2, rtol=1e-4)
607 # Try with an elliptical aperture. This is a proxy for
608 # optimal aperture, since we do not actually measure anything.
609 aperShape = afwGeom.Quadrupole(8, 6, 3)
610 fluxErrScaling1 = algorithm._getFluxErrScaling(kernelAcf, aperShape)
611 fluxErrScaling2 = self.getFluxErrScaling(kernel, aperShape)
612 self.assertFloatsAlmostEqual(fluxErrScaling1, fluxErrScaling2, rtol=1e-4)
614 @lsst.utils.tests.methodParameters(noise=(0.001, 0.01, 0.1))
615 def testMonteCarlo(self, noise, recordId=1, sigmas=[0.7, 1.0, 1.25],
616 scalingFactors=[1.1, 1.15, 1.2, 1.3, 1.4]):
617 """Test GAaP flux uncertainties.
619 This test should demonstate that the estimated flux uncertainties agree
620 with those from Monte Carlo simulations.
622 Parameters
623 ----------
624 noise : `float`
625 The RMS value of the Gaussian noise field divided by the total flux
626 of the source.
627 recordId : `int`, optional
628 The source Id in the test dataset to measure.
629 sigmas : `list` [`float`], optional
630 The list of sigmas (in pixels) to construct the `GaapFluxConfig`.
631 scalingFactors : `list` [`float`], optional
632 The list of scaling factors to construct the `GaapFluxConfig`.
633 """
634 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas,
635 scalingFactors=scalingFactors)
636 gaapConfig.scaleByFwhm = True
637 gaapConfig.doPsfPhotometry = True
638 gaapConfig.doOptimalPhotometry = True
640 algorithm, schema = self.makeAlgorithm(gaapConfig)
641 # Make a noiseless exposure and keep measurement record for reference
642 exposure, catalog = self.dataset.realize(0.0, schema)
643 if gaapConfig.doOptimalPhotometry:
644 self.recordPsfShape(catalog)
645 recordNoiseless = catalog[recordId]
646 totalFlux = recordNoiseless["truth_instFlux"]
647 algorithm.measure(recordNoiseless, exposure)
649 nSamples = 1024
650 catalog = afwTable.SourceCatalog(schema)
651 for repeat in range(nSamples):
652 exposure, cat = self.dataset.realize(noise*totalFlux, schema, randomSeed=repeat)
653 if gaapConfig.doOptimalPhotometry:
654 self.recordPsfShape(cat)
655 record = cat[recordId]
656 algorithm.measure(record, exposure)
657 catalog.append(record)
659 catalog = catalog.copy(deep=True)
660 for baseName in gaapConfig.getAllGaapResultNames():
661 instFluxKey = schema.join(baseName, "instFlux")
662 instFluxErrKey = schema.join(baseName, "instFluxErr")
663 instFluxMean = catalog[instFluxKey].mean()
664 instFluxErrMean = catalog[instFluxErrKey].mean()
665 instFluxStdDev = catalog[instFluxKey].std()
667 # GAaP fluxes are not meant to be total fluxes.
668 # We compare the mean of the noisy measurements to its
669 # corresponding noiseless measurement instead of the true value
670 instFlux = recordNoiseless[instFluxKey]
671 self.assertFloatsAlmostEqual(instFluxErrMean, instFluxStdDev, rtol=0.02)
672 self.assertLess(abs(instFluxMean - instFlux), 2.0*instFluxErrMean/nSamples**0.5)
675class TestMemory(lsst.utils.tests.MemoryTestCase):
676 pass
679def setup_module(module, backend="virtualDevice"):
680 lsst.utils.tests.init()
681 try:
682 afwDisplay.setDefaultBackend(backend)
683 except Exception:
684 print("Unable to configure display backend: %s" % backend)
687if __name__ == "__main__": 687 ↛ 688line 687 didn't jump to line 688, because the condition on line 687 was never true
688 import sys
690 from argparse import ArgumentParser
691 parser = ArgumentParser()
692 parser.add_argument('--backend', type=str, default="virtualDevice",
693 help="The backend to use, e.g. 'ds9'. Be sure to 'setup display_<backend>'")
694 args = parser.parse_args()
696 setup_module(sys.modules[__name__], backend=args.backend)
697 unittest.main()