Coverage for tests/test_gaap.py: 14%
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1# This file is part of meas_extensions_gaap
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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 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 )
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"])
392 # Set an empty footprint and check that no_pixels flag is set.
393 record = catalog[1]
394 record.setFootprint(afwDetection.Footprint())
395 with self.assertRaises(lsst.meas.extensions.gaap._gaap.NoPixelError):
396 algorithm.measure(record, exposure)
397 self.assertTrue(record[algName + "_flag"])
398 self.assertTrue(record[algName + "_flag_no_pixel"])
400 # Ensure that the edge flag is set for the source at the corner.
401 record = catalog[2]
402 algorithm.measure(record, exposure)
403 self.assertTrue(record[algName + "_flag_edge"])
404 self.assertFalse(record[algName + "_flag"])
406 def recordPsfShape(self, catalog) -> None:
407 """Record PSF shapes under the appropriate fields in ``catalog``.
409 This method must be called after the dataset is realized and a catalog
410 is returned by the `realize` method. It assumes that the schema is
411 non-minimal and has "psfShape_xx", "psfShape_yy" and "psfShape_xy"
412 fields setup
414 Parameters
415 ----------
416 catalog : `~lsst.afw.table.SourceCatalog`
417 A source catalog containing records of the simulated sources.
418 """
419 psfShapeKey = afwTable.QuadrupoleKey(catalog.schema["slot_PsfShape"])
420 for record in catalog:
421 record.set(psfShapeKey, self.dataset.psfShape)
423 @staticmethod
424 def invertQuadrupole(shape: afwGeom.Quadrupole) -> afwGeom.Quadrupole:
425 """Compute the Quadrupole object corresponding to the inverse matrix.
427 If M = [[Q.getIxx(), Q.getIxy()],
428 [Q.getIxy(), Q.getIyy()]]
430 for the input quadrupole Q, the returned quadrupole R corresponds to
432 M^{-1} = [[R.getIxx(), R.getIxy()],
433 [R.getIxy(), R.getIyy()]].
434 """
435 invShape = afwGeom.Quadrupole(shape.getIyy(), shape.getIxx(), -shape.getIxy())
436 invShape.scale(1./shape.getDeterminantRadius()**2)
437 return invShape
439 @lsst.utils.tests.methodParameters(gaussianizationMethod=("auto", "overlap-add", "direct", "fft"))
440 def testGalaxyPhotometry(self, gaussianizationMethod):
441 """Test GAaP fluxes for extended sources.
443 Create and run a SingleFrameMeasurementTask with GAaP plugin and reuse
444 its outputs as reference for ForcedGaapFluxPlugin. In both cases,
445 the measured flux is compared with the analytical expectation.
447 For a Gaussian source with intrinsic shape S and intrinsic aperture W,
448 the GAaP flux is defined as (Eq. A16 of Kuijken et al. 2015)
449 :math:`\\frac{F}{2\\pi\\det(S)}\\int\\mathrm{d}x\\exp(-x^T(S^{-1}+W^{-1})x/2)`
450 :math:`F\\frac{\\det(S^{-1})}{\\det(S^{-1}+W^{-1})}`
451 """
452 algName = "ext_gaap_GaapFlux"
453 dependencies = ("base_SdssShape",)
454 sfmConfig = self.makeSingleFrameMeasurementConfig(algName, dependencies=dependencies)
455 forcedConfig = self.makeForcedMeasurementConfig(algName, dependencies=dependencies)
456 # Turn on optimal photometry explicitly
457 sfmConfig.plugins[algName].doOptimalPhotometry = True
458 forcedConfig.plugins[algName].doOptimalPhotometry = True
459 sfmConfig.plugins[algName].gaussianizationMethod = gaussianizationMethod
460 forcedConfig.plugins[algName].gaussianizationMethod = gaussianizationMethod
462 algMetadata = lsst.daf.base.PropertyList()
463 sfmTask = self.makeSingleFrameMeasurementTask(config=sfmConfig, algMetadata=algMetadata)
464 forcedTask = self.makeForcedMeasurementTask(config=forcedConfig, algMetadata=algMetadata,
465 refSchema=sfmTask.schema)
467 refExposure, refCatalog = self.dataset.realize(0.0, sfmTask.schema)
468 self.recordPsfShape(refCatalog)
469 sfmTask.run(refCatalog, refExposure)
471 # Check if the measured values match the expectations from
472 # analytical Gaussian integrals
473 recordId = 1 # Elliptical Gaussian galaxy
474 refRecord = refCatalog[recordId]
475 refWcs = self.dataset.exposure.getWcs()
476 schema = refRecord.schema
477 trueFlux = refRecord["truth_instFlux"]
478 intrinsicShapeVector = afwTable.QuadrupoleKey(schema["truth"]).get(refRecord).getParameterVector() \
479 - afwTable.QuadrupoleKey(schema["slot_PsfShape"]).get(refRecord).getParameterVector()
480 intrinsicShape = afwGeom.Quadrupole(intrinsicShapeVector)
481 invIntrinsicShape = self.invertQuadrupole(intrinsicShape)
482 # Assert that the measured fluxes agree with analytical expectations.
483 for sigma in sfmTask.config.plugins[algName]._sigmas:
484 if sigma == "Optimal":
485 aperShape = afwTable.QuadrupoleKey(schema[f"{algName}_OptimalShape"]).get(refRecord)
486 else:
487 aperShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
488 aperShape.transformInPlace(refWcs.linearizeSkyToPixel(refRecord.getCentroid(),
489 geom.arcseconds).getLinear())
491 invAperShape = self.invertQuadrupole(aperShape)
492 analyticalFlux = trueFlux*(invIntrinsicShape.getDeterminantRadius()
493 / invIntrinsicShape.convolve(invAperShape).getDeterminantRadius())**2
494 for scalingFactor in sfmTask.config.plugins[algName].scalingFactors:
495 baseName = sfmTask.plugins[algName].ConfigClass._getGaapResultName(scalingFactor,
496 sigma, algName)
497 instFlux = refRecord.get(f"{baseName}_instFlux")
498 self.assertFloatsAlmostEqual(instFlux, analyticalFlux, rtol=5e-3)
500 measWcs = self.dataset.makePerturbedWcs(refWcs, randomSeed=15)
501 measDataset = self.dataset.transform(measWcs)
502 measExposure, truthCatalog = measDataset.realize(0.0, schema)
503 measCatalog = forcedTask.generateMeasCat(measExposure, refCatalog, refWcs)
504 forcedTask.attachTransformedFootprints(measCatalog, refCatalog, measExposure, refWcs)
505 forcedTask.run(measCatalog, measExposure, refCatalog, refWcs)
507 fullTransform = afwGeom.makeWcsPairTransform(refWcs, measWcs)
508 localTransform = afwGeom.linearizeTransform(fullTransform, refRecord.getCentroid()).getLinear()
509 intrinsicShape.transformInPlace(localTransform)
510 invIntrinsicShape = self.invertQuadrupole(intrinsicShape)
511 measRecord = measCatalog[recordId]
513 # Since measCatalog and refCatalog differ only by WCS, the GAaP flux
514 # measured through consistent apertures must agree with each other.
515 for sigma in forcedTask.config.plugins[algName]._sigmas:
516 if sigma == "Optimal":
517 aperShape = afwTable.QuadrupoleKey(measRecord.schema[f"{algName}_"
518 "OptimalShape"]).get(measRecord)
519 else:
520 aperShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
521 aperShape.transformInPlace(measWcs.linearizeSkyToPixel(measRecord.getCentroid(),
522 geom.arcseconds).getLinear())
524 invAperShape = self.invertQuadrupole(aperShape)
525 analyticalFlux = trueFlux*(invIntrinsicShape.getDeterminantRadius()
526 / invIntrinsicShape.convolve(invAperShape).getDeterminantRadius())**2
527 for scalingFactor in forcedTask.config.plugins[algName].scalingFactors:
528 baseName = forcedTask.plugins[algName].ConfigClass._getGaapResultName(scalingFactor,
529 sigma, algName)
530 instFlux = measRecord.get(f"{baseName}_instFlux")
531 # The measurement in the measRecord must be consistent with
532 # the same in the refRecord in addition to analyticalFlux.
533 self.assertFloatsAlmostEqual(instFlux, refRecord.get(f"{baseName}_instFlux"), rtol=5e-3)
534 self.assertFloatsAlmostEqual(instFlux, analyticalFlux, rtol=5e-3)
536 def getFluxErrScaling(self, kernel, aperShape):
537 """Returns the value by which the standard error has to be scaled due
538 to noise correlations.
540 This is an alternative implementation to the `_getFluxErrScaling`
541 method of `BaseGaapFluxPlugin`, but is less efficient.
543 Parameters
544 ----------
545 `kernel` : `~lsst.afw.math.Kernel`
546 The PSF-Gaussianization kernel.
548 Returns
549 -------
550 fluxErrScaling : `float`
551 The factor by which the standard error on GAaP flux must be scaled.
552 """
553 kim = afwImage.ImageD(kernel.getDimensions())
554 kernel.computeImage(kim, False)
555 weight = galsim.Image(np.zeros_like(kim.array))
556 aperSigma = aperShape.getDeterminantRadius()
557 trace = aperShape.getIxx() + aperShape.getIyy()
558 distortion = galsim.Shear(e1=(aperShape.getIxx()-aperShape.getIyy())/trace,
559 e2=2*aperShape.getIxy()/trace)
560 gauss = galsim.Gaussian(sigma=aperSigma, flux=2*np.pi*aperSigma**2).shear(distortion)
561 weight = gauss.drawImage(image=weight, scale=1.0, method='no_pixel')
562 kwarr = scipy.signal.convolve2d(weight.array, kim.array, boundary='fill')
563 fluxErrScaling = np.sqrt(np.sum(kwarr*kwarr))
564 fluxErrScaling /= np.sqrt(np.pi*aperSigma**2)
565 return fluxErrScaling
567 def testCorrelatedNoiseError(self, sigmas=[0.6, 0.8], scalingFactors=[1.15, 1.2, 1.25, 1.3, 1.4]):
568 """Test the scaling to standard error due to correlated noise.
570 The uncertainty estimate on GAaP fluxes is scaled by an amount
571 determined by the auto-correlation function of the PSF-matching kernel;
572 see Eqs. A11 & A17 of Kuijken et al. (2015). This test ensures that the
573 calculation of the scaling factors matches the analytical expression
574 when the PSF-matching kernel is a Gaussian.
576 Parameters
577 ----------
578 sigmas : `list` [`float`], optional
579 A list of effective Gaussian aperture sizes.
580 scalingFactors : `list` [`float`], optional
581 A list of factors by which the PSF size must be scaled.
583 Notes
584 -----
585 This unit test tests internal states of the plugin for accuracy and is
586 specific to the implementation. It uses private variables as a result
587 and intentionally breaks encapsulation.
588 """
589 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas,
590 scalingFactors=scalingFactors)
591 gaapConfig.scaleByFwhm = True
593 algorithm, schema = self.makeAlgorithm(gaapConfig)
594 exposure, catalog = self.dataset.realize(0.0, schema)
595 wcs = exposure.getWcs()
596 record = catalog[0]
597 center = self.center
598 seeing = exposure.getPsf().computeShape(center).getDeterminantRadius()
599 for scalingFactor in gaapConfig.scalingFactors:
600 targetSigma = scalingFactor*seeing
601 modelPsf = afwDetection.GaussianPsf(algorithm.config._modelPsfDimension,
602 algorithm.config._modelPsfDimension,
603 targetSigma)
604 result = algorithm._gaussianize(exposure, modelPsf, record)
605 kernel = result.psfMatchingKernel
606 kernelAcf = algorithm._computeKernelAcf(kernel)
607 for sigma in gaapConfig.sigmas:
608 intrinsicShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
609 intrinsicShape.transformInPlace(wcs.linearizeSkyToPixel(center, geom.arcseconds).getLinear())
610 aperShape = afwGeom.Quadrupole(intrinsicShape.getParameterVector()
611 - [targetSigma**2, targetSigma**2, 0.0])
612 fluxErrScaling1 = algorithm._getFluxErrScaling(kernelAcf, aperShape)
613 fluxErrScaling2 = self.getFluxErrScaling(kernel, aperShape)
615 # The PSF matching kernel is a Gaussian of sigma^2 = (f^2-1)s^2
616 # where f is the scalingFactor and s is the original seeing.
617 # The integral of ACF of the kernel times the elliptical
618 # Gaussian described by aperShape is given below.
619 sigma /= wcs.getPixelScale().asArcseconds()
620 analyticalValue = ((sigma**2 - (targetSigma)**2)/(sigma**2-seeing**2))**0.5
621 self.assertFloatsAlmostEqual(fluxErrScaling1, analyticalValue, rtol=1e-4)
622 self.assertFloatsAlmostEqual(fluxErrScaling1, fluxErrScaling2, rtol=1e-4)
624 # Try with an elliptical aperture. This is a proxy for
625 # optimal aperture, since we do not actually measure anything.
626 aperShape = afwGeom.Quadrupole(8, 6, 3)
627 fluxErrScaling1 = algorithm._getFluxErrScaling(kernelAcf, aperShape)
628 fluxErrScaling2 = self.getFluxErrScaling(kernel, aperShape)
629 self.assertFloatsAlmostEqual(fluxErrScaling1, fluxErrScaling2, rtol=1e-4)
631 @lsst.utils.tests.methodParameters(noise=(0.001, 0.01, 0.1))
632 def testMonteCarlo(self, noise, recordId=1, sigmas=[0.7, 1.0, 1.25],
633 scalingFactors=[1.1, 1.15, 1.2, 1.3, 1.4]):
634 """Test GAaP flux uncertainties.
636 This test should demonstate that the estimated flux uncertainties agree
637 with those from Monte Carlo simulations.
639 Parameters
640 ----------
641 noise : `float`
642 The RMS value of the Gaussian noise field divided by the total flux
643 of the source.
644 recordId : `int`, optional
645 The source Id in the test dataset to measure.
646 sigmas : `list` [`float`], optional
647 The list of sigmas (in pixels) to construct the `GaapFluxConfig`.
648 scalingFactors : `list` [`float`], optional
649 The list of scaling factors to construct the `GaapFluxConfig`.
650 """
651 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas,
652 scalingFactors=scalingFactors)
653 gaapConfig.scaleByFwhm = True
654 gaapConfig.doPsfPhotometry = True
655 gaapConfig.doOptimalPhotometry = True
657 algorithm, schema = self.makeAlgorithm(gaapConfig)
658 # Make a noiseless exposure and keep measurement record for reference
659 exposure, catalog = self.dataset.realize(0.0, schema)
660 if gaapConfig.doOptimalPhotometry:
661 self.recordPsfShape(catalog)
662 recordNoiseless = catalog[recordId]
663 totalFlux = recordNoiseless["truth_instFlux"]
664 algorithm.measure(recordNoiseless, exposure)
666 nSamples = 1024
667 catalog = afwTable.SourceCatalog(schema)
668 for repeat in range(nSamples):
669 exposure, cat = self.dataset.realize(noise*totalFlux, schema, randomSeed=repeat)
670 if gaapConfig.doOptimalPhotometry:
671 self.recordPsfShape(cat)
672 record = cat[recordId]
673 algorithm.measure(record, exposure)
674 catalog.append(record)
676 catalog = catalog.copy(deep=True)
677 for baseName in gaapConfig.getAllGaapResultNames():
678 instFluxKey = schema.join(baseName, "instFlux")
679 instFluxErrKey = schema.join(baseName, "instFluxErr")
680 instFluxMean = catalog[instFluxKey].mean()
681 instFluxErrMean = catalog[instFluxErrKey].mean()
682 instFluxStdDev = catalog[instFluxKey].std()
684 # GAaP fluxes are not meant to be total fluxes.
685 # We compare the mean of the noisy measurements to its
686 # corresponding noiseless measurement instead of the true value
687 instFlux = recordNoiseless[instFluxKey]
688 self.assertFloatsAlmostEqual(instFluxErrMean, instFluxStdDev, rtol=0.02)
689 self.assertLess(abs(instFluxMean - instFlux), 2.0*instFluxErrMean/nSamples**0.5)
692class TestMemory(lsst.utils.tests.MemoryTestCase):
693 pass
696def setup_module(module, backend="virtualDevice"):
697 lsst.utils.tests.init()
698 try:
699 afwDisplay.setDefaultBackend(backend)
700 except Exception:
701 print("Unable to configure display backend: %s" % backend)
704if __name__ == "__main__": 704 ↛ 705line 704 didn't jump to line 705, because the condition on line 704 was never true
705 import sys
707 from argparse import ArgumentParser
708 parser = ArgumentParser()
709 parser.add_argument('--backend', type=str, default="virtualDevice",
710 help="The backend to use, e.g. 'ds9'. Be sure to 'setup display_<backend>'")
711 args = parser.parse_args()
713 setup_module(sys.modules[__name__], backend=args.backend)
714 unittest.main()