Coverage for tests/test_gaap.py : 12%

Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
1# This file is part of meas_extensions_gaap
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (http://www.lsst.org/).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the LSST License Statement and
20# the GNU General Public License along with this program. If not,
21# see <http://www.lsstcorp.org/LegalNotices/>.
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.
240 For failure modes not handled by the `fail` method, we test them
241 in the ``testFlags`` method.
242 """
243 algName = "ext_gaap_GaapFlux"
244 dependencies = ("base_SdssShape",)
245 config = self.makeSingleFrameMeasurementConfig(algName, dependencies=dependencies)
246 gaapConfig = config.plugins[algName]
247 gaapConfig.scalingFactors = scalingFactors
248 gaapConfig.sigmas = sigmas
249 gaapConfig.doPsfPhotometry = True
250 gaapConfig.doOptimalPhotometry = True
252 gaapConfig.scaleByFwhm = True
253 self.assertTrue(gaapConfig.scaleByFwhm) # Test the getter method.
255 algMetadata = lsst.daf.base.PropertyList()
256 sfmTask = self.makeSingleFrameMeasurementTask(algName, dependencies=dependencies, config=config,
257 algMetadata=algMetadata)
258 exposure, catalog = self.dataset.realize(0.0, sfmTask.schema)
259 self.recordPsfShape(catalog)
260 sfmTask.run(catalog, exposure)
262 for record in catalog:
263 self.assertFalse(record[algName + "_flag"])
264 for scalingFactor in scalingFactors:
265 flagName = gaapConfig._getGaapResultName(scalingFactor, "flag_gaussianization", algName)
266 self.assertTrue(record[flagName])
267 for sigma in sigmas + ["Optimal"]:
268 baseName = gaapConfig._getGaapResultName(scalingFactor, sigma, algName)
269 self.assertTrue(record[baseName + "_flag"])
270 self.assertFalse(record[baseName + "_flag_bigPsf"])
272 baseName = gaapConfig._getGaapResultName(scalingFactor, "PsfFlux", algName)
273 self.assertTrue(record[baseName + "_flag"])
275 # Try and "fail" with no PSF.
276 # Since fatal exceptions are not caught by the measurement framework,
277 # use a context manager and catch it here.
278 exposure.setPsf(None)
279 with self.assertRaises(lsst.meas.base.FatalAlgorithmError):
280 sfmTask.run(catalog, exposure)
282 def testFlags(self, sigmas=[0.4, 0.5, 0.7], scalingFactors=[1.15, 1.25, 1.4, 100.]):
283 """Test that GAaP flags are set properly.
285 Specifically, we test that
287 1. for invalid combinations of config parameters, only the
288 appropriate flags are set and not that the entire measurement itself is
289 flagged.
290 2. for sources close to the edge, the edge flags are set.
292 Parameters
293 ----------
294 sigmas : `list` [`float`], optional
295 The list of sigmas (in arcseconds) to construct the
296 `SingleFrameGaapFluxConfig`.
297 scalingFactors : `list` [`float`], optional
298 The list of scaling factors to construct the
299 `SingleFrameGaapFluxConfig`.
301 Raises
302 -----
303 InvalidParameterError
304 Raised if none of the config parameters will fail a measurement.
306 Notes
307 -----
308 Since the seeing in the test dataset is 2 pixels, at least one of the
309 ``sigmas`` should be smaller than at least twice of one of the
310 ``scalingFactors`` to avoid the InvalidParameterError exception being
311 raised.
312 """
313 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas,
314 scalingFactors=scalingFactors)
315 gaapConfig.scaleByFwhm = True
316 gaapConfig.doOptimalPhotometry = True
318 # Make an instance of GAaP algorithm from a config
319 algName = "ext_gaap_GaapFlux"
320 algorithm, schema = self.makeAlgorithm(gaapConfig)
321 # Make a noiseless exposure and measurements for reference
322 exposure, catalog = self.dataset.realize(0.0, schema)
323 # Record the PSF shapes if optimal photometry is performed.
324 if gaapConfig.doOptimalPhotometry:
325 self.recordPsfShape(catalog)
327 record = catalog[0]
328 algorithm.measure(record, exposure)
329 seeing = exposure.getPsf().getSigma()
330 pixelScale = exposure.getWcs().getPixelScale().asArcseconds()
331 # Measurement must fail (i.e., flag_bigPsf and flag must be set) if
332 # sigma < scalingFactor * seeing
333 # Ensure that there is at least one combination of parameters that fail
334 if not(min(gaapConfig.sigmas)/pixelScale < seeing*max(gaapConfig.scalingFactors)):
335 raise InvalidParameterError("The config parameters do not trigger a measurement failure. "
336 "Consider including lower values in ``sigmas`` and/or larger values "
337 "for ``scalingFactors``")
338 # Ensure that the measurement is not a complete failure
339 self.assertFalse(record[algName + "_flag"])
340 self.assertFalse(record[algName + "_flag_edge"])
341 # Ensure that flag_bigPsf is set if sigma < scalingFactor * seeing
342 for scalingFactor, sigma in itertools.product(gaapConfig.scalingFactors, gaapConfig.sigmas):
343 targetSigma = scalingFactor*seeing
344 baseName = gaapConfig._getGaapResultName(scalingFactor, sigma, algName)
345 # Give some leeway for the edge case.
346 if targetSigma - sigma/pixelScale >= -1e-10:
347 self.assertTrue(record[baseName+"_flag_bigPsf"])
348 self.assertTrue(record[baseName+"_flag"])
349 else:
350 self.assertFalse(record[baseName+"_flag_bigPsf"])
351 self.assertFalse(record[baseName+"_flag"])
353 # Ensure that flag_bigPsf is set if OptimalShape is not large enough.
354 if gaapConfig.doOptimalPhotometry:
355 aperShape = afwTable.QuadrupoleKey(schema[schema.join(algName, "OptimalShape")]).get(record)
356 for scalingFactor in gaapConfig.scalingFactors:
357 targetSigma = scalingFactor*seeing
358 baseName = gaapConfig._getGaapResultName(scalingFactor, "Optimal", algName)
359 try:
360 afwGeom.Quadrupole(aperShape.getParameterVector()-[targetSigma**2, targetSigma**2, 0.0],
361 normalize=True)
362 self.assertFalse(record[baseName + "_flag_bigPsf"])
363 except InvalidParameterError:
364 self.assertTrue(record[baseName + "_flag_bigPsf"])
366 # Ensure that the edge flag is set for the source at the corner.
367 record = catalog[2]
368 algorithm.measure(record, exposure)
369 self.assertTrue(record[algName + "_flag_edge"])
370 self.assertFalse(record[algName + "_flag"])
372 def recordPsfShape(self, catalog) -> None:
373 """Record PSF shapes under the appropriate fields in ``catalog``.
375 This method must be called after the dataset is realized and a catalog
376 is returned by the `realize` method. It assumes that the schema is
377 non-minimal and has "psfShape_xx", "psfShape_yy" and "psfShape_xy"
378 fields setup
380 Parameters
381 ----------
382 catalog : `~lsst.afw.table.SourceCatalog`
383 A source catalog containing records of the simulated sources.
384 """
385 psfShapeKey = afwTable.QuadrupoleKey(catalog.schema["slot_PsfShape"])
386 for record in catalog:
387 record.set(psfShapeKey, self.dataset.psfShape)
389 @staticmethod
390 def invertQuadrupole(shape: afwGeom.Quadrupole) -> afwGeom.Quadrupole:
391 """Compute the Quadrupole object corresponding to the inverse matrix.
393 If M = [[Q.getIxx(), Q.getIxy()],
394 [Q.getIxy(), Q.getIyy()]]
396 for the input quadrupole Q, the returned quadrupole R corresponds to
398 M^{-1} = [[R.getIxx(), R.getIxy()],
399 [R.getIxy(), R.getIyy()]].
400 """
401 invShape = afwGeom.Quadrupole(shape.getIyy(), shape.getIxx(), -shape.getIxy())
402 invShape.scale(1./shape.getDeterminantRadius()**2)
403 return invShape
405 @lsst.utils.tests.methodParameters(gaussianizationMethod=("auto", "overlap-add", "direct", "fft"))
406 def testGalaxyPhotometry(self, gaussianizationMethod):
407 """Test GAaP fluxes for extended sources.
409 Create and run a SingleFrameMeasurementTask with GAaP plugin and reuse
410 its outputs as reference for ForcedGaapFluxPlugin. In both cases,
411 the measured flux is compared with the analytical expectation.
413 For a Gaussian source with intrinsic shape S and intrinsic aperture W,
414 the GAaP flux is defined as (Eq. A16 of Kuijken et al. 2015)
415 :math:`\\frac{F}{2\\pi\\det(S)}\\int\\mathrm{d}x\\exp(-x^T(S^{-1}+W^{-1})x/2)`
416 :math:`F\\frac{\\det(S^{-1})}{\\det(S^{-1}+W^{-1})}`
417 """
418 algName = "ext_gaap_GaapFlux"
419 dependencies = ("base_SdssShape",)
420 sfmConfig = self.makeSingleFrameMeasurementConfig(algName, dependencies=dependencies)
421 forcedConfig = self.makeForcedMeasurementConfig(algName, dependencies=dependencies)
422 # Turn on optimal photometry explicitly
423 sfmConfig.plugins[algName].doOptimalPhotometry = True
424 forcedConfig.plugins[algName].doOptimalPhotometry = True
425 sfmConfig.plugins[algName].gaussianizationMethod = gaussianizationMethod
426 forcedConfig.plugins[algName].gaussianizationMethod = gaussianizationMethod
428 algMetadata = lsst.daf.base.PropertyList()
429 sfmTask = self.makeSingleFrameMeasurementTask(config=sfmConfig, algMetadata=algMetadata)
430 forcedTask = self.makeForcedMeasurementTask(config=forcedConfig, algMetadata=algMetadata,
431 refSchema=sfmTask.schema)
433 refExposure, refCatalog = self.dataset.realize(0.0, sfmTask.schema)
434 self.recordPsfShape(refCatalog)
435 sfmTask.run(refCatalog, refExposure)
437 # Check if the measured values match the expectations from
438 # analytical Gaussian integrals
439 recordId = 1 # Elliptical Gaussian galaxy
440 refRecord = refCatalog[recordId]
441 refWcs = self.dataset.exposure.getWcs()
442 schema = refRecord.schema
443 trueFlux = refRecord["truth_instFlux"]
444 intrinsicShapeVector = afwTable.QuadrupoleKey(schema["truth"]).get(refRecord).getParameterVector() \
445 - afwTable.QuadrupoleKey(schema["slot_PsfShape"]).get(refRecord).getParameterVector()
446 intrinsicShape = afwGeom.Quadrupole(intrinsicShapeVector)
447 invIntrinsicShape = self.invertQuadrupole(intrinsicShape)
448 # Assert that the measured fluxes agree with analytical expectations.
449 for sigma in sfmTask.config.plugins[algName]._sigmas:
450 if sigma == "Optimal":
451 aperShape = afwTable.QuadrupoleKey(schema[f"{algName}_OptimalShape"]).get(refRecord)
452 else:
453 aperShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
454 aperShape.transformInPlace(refWcs.linearizeSkyToPixel(refRecord.getCentroid(),
455 geom.arcseconds).getLinear())
457 invAperShape = self.invertQuadrupole(aperShape)
458 analyticalFlux = trueFlux*(invIntrinsicShape.getDeterminantRadius()
459 / invIntrinsicShape.convolve(invAperShape).getDeterminantRadius())**2
460 for scalingFactor in sfmTask.config.plugins[algName].scalingFactors:
461 baseName = sfmTask.plugins[algName].ConfigClass._getGaapResultName(scalingFactor,
462 sigma, algName)
463 instFlux = refRecord.get(f"{baseName}_instFlux")
464 self.assertFloatsAlmostEqual(instFlux, analyticalFlux, rtol=5e-3)
466 measWcs = self.dataset.makePerturbedWcs(refWcs, randomSeed=15)
467 measDataset = self.dataset.transform(measWcs)
468 measExposure, truthCatalog = measDataset.realize(0.0, schema)
469 measCatalog = forcedTask.generateMeasCat(measExposure, refCatalog, refWcs)
470 forcedTask.attachTransformedFootprints(measCatalog, refCatalog, measExposure, refWcs)
471 forcedTask.run(measCatalog, measExposure, refCatalog, refWcs)
473 fullTransform = afwGeom.makeWcsPairTransform(refWcs, measWcs)
474 localTransform = afwGeom.linearizeTransform(fullTransform, refRecord.getCentroid()).getLinear()
475 intrinsicShape.transformInPlace(localTransform)
476 invIntrinsicShape = self.invertQuadrupole(intrinsicShape)
477 measRecord = measCatalog[recordId]
479 # Since measCatalog and refCatalog differ only by WCS, the GAaP flux
480 # measured through consistent apertures must agree with each other.
481 for sigma in forcedTask.config.plugins[algName]._sigmas:
482 if sigma == "Optimal":
483 aperShape = afwTable.QuadrupoleKey(measRecord.schema[f"{algName}_"
484 "OptimalShape"]).get(measRecord)
485 else:
486 aperShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
487 aperShape.transformInPlace(measWcs.linearizeSkyToPixel(measRecord.getCentroid(),
488 geom.arcseconds).getLinear())
490 invAperShape = self.invertQuadrupole(aperShape)
491 analyticalFlux = trueFlux*(invIntrinsicShape.getDeterminantRadius()
492 / invIntrinsicShape.convolve(invAperShape).getDeterminantRadius())**2
493 for scalingFactor in forcedTask.config.plugins[algName].scalingFactors:
494 baseName = forcedTask.plugins[algName].ConfigClass._getGaapResultName(scalingFactor,
495 sigma, algName)
496 instFlux = measRecord.get(f"{baseName}_instFlux")
497 # The measurement in the measRecord must be consistent with
498 # the same in the refRecord in addition to analyticalFlux.
499 self.assertFloatsAlmostEqual(instFlux, refRecord.get(f"{baseName}_instFlux"), rtol=5e-3)
500 self.assertFloatsAlmostEqual(instFlux, analyticalFlux, rtol=5e-3)
502 def getFluxErrScaling(self, kernel, aperShape):
503 """Returns the value by which the standard error has to be scaled due
504 to noise correlations.
506 This is an alternative implementation to the `_getFluxErrScaling`
507 method of `BaseGaapFluxPlugin`, but is less efficient.
509 Parameters
510 ----------
511 `kernel` : `~lsst.afw.math.Kernel`
512 The PSF-Gaussianization kernel.
514 Returns
515 -------
516 fluxErrScaling : `float`
517 The factor by which the standard error on GAaP flux must be scaled.
518 """
519 kim = afwImage.ImageD(kernel.getDimensions())
520 kernel.computeImage(kim, False)
521 weight = galsim.Image(np.zeros_like(kim.array))
522 aperSigma = aperShape.getDeterminantRadius()
523 trace = aperShape.getIxx() + aperShape.getIyy()
524 distortion = galsim.Shear(e1=(aperShape.getIxx()-aperShape.getIyy())/trace,
525 e2=2*aperShape.getIxy()/trace)
526 gauss = galsim.Gaussian(sigma=aperSigma, flux=2*np.pi*aperSigma**2).shear(distortion)
527 weight = gauss.drawImage(image=weight, scale=1.0, method='no_pixel')
528 kwarr = scipy.signal.convolve2d(weight.array, kim.array, boundary='fill')
529 fluxErrScaling = np.sqrt(np.sum(kwarr*kwarr))
530 fluxErrScaling /= np.sqrt(np.pi*aperSigma**2)
531 return fluxErrScaling
533 def testCorrelatedNoiseError(self, sigmas=[0.6, 0.8], scalingFactors=[1.15, 1.2, 1.25, 1.3, 1.4]):
534 """Test the scaling to standard error due to correlated noise.
536 The uncertainty estimate on GAaP fluxes is scaled by an amount
537 determined by the auto-correlation function of the PSF-matching kernel;
538 see Eqs. A11 & A17 of Kuijken et al. (2015). This test ensures that the
539 calculation of the scaling factors matches the analytical expression
540 when the PSF-matching kernel is a Gaussian.
542 Parameters
543 ----------
544 sigmas : `list` [`float`], optional
545 A list of effective Gaussian aperture sizes.
546 scalingFactors : `list` [`float`], optional
547 A list of factors by which the PSF size must be scaled.
549 Notes
550 -----
551 This unit test tests internal states of the plugin for accuracy and is
552 specific to the implementation. It uses private variables as a result
553 and intentionally breaks encapsulation.
554 """
555 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas,
556 scalingFactors=scalingFactors)
557 gaapConfig.scaleByFwhm = True
559 algorithm, schema = self.makeAlgorithm(gaapConfig)
560 exposure, catalog = self.dataset.realize(0.0, schema)
561 wcs = exposure.getWcs()
562 record = catalog[0]
563 center = self.center
564 seeing = exposure.getPsf().computeShape(center).getDeterminantRadius()
565 for scalingFactor in gaapConfig.scalingFactors:
566 targetSigma = scalingFactor*seeing
567 modelPsf = afwDetection.GaussianPsf(algorithm.config._modelPsfDimension,
568 algorithm.config._modelPsfDimension,
569 targetSigma)
570 result = algorithm._gaussianize(exposure, modelPsf, record)
571 kernel = result.psfMatchingKernel
572 kernelAcf = algorithm._computeKernelAcf(kernel)
573 for sigma in gaapConfig.sigmas:
574 intrinsicShape = afwGeom.Quadrupole(sigma**2, sigma**2, 0.0)
575 intrinsicShape.transformInPlace(wcs.linearizeSkyToPixel(center, geom.arcseconds).getLinear())
576 aperShape = afwGeom.Quadrupole(intrinsicShape.getParameterVector()
577 - [targetSigma**2, targetSigma**2, 0.0])
578 fluxErrScaling1 = algorithm._getFluxErrScaling(kernelAcf, aperShape)
579 fluxErrScaling2 = self.getFluxErrScaling(kernel, aperShape)
581 # The PSF matching kernel is a Gaussian of sigma^2 = (f^2-1)s^2
582 # where f is the scalingFactor and s is the original seeing.
583 # The integral of ACF of the kernel times the elliptical
584 # Gaussian described by aperShape is given below.
585 sigma /= wcs.getPixelScale().asArcseconds()
586 analyticalValue = ((sigma**2 - (targetSigma)**2)/(sigma**2-seeing**2))**0.5
587 self.assertFloatsAlmostEqual(fluxErrScaling1, analyticalValue, rtol=1e-4)
588 self.assertFloatsAlmostEqual(fluxErrScaling1, fluxErrScaling2, rtol=1e-4)
590 # Try with an elliptical aperture. This is a proxy for
591 # optimal aperture, since we do not actually measure anything.
592 aperShape = afwGeom.Quadrupole(8, 6, 3)
593 fluxErrScaling1 = algorithm._getFluxErrScaling(kernelAcf, aperShape)
594 fluxErrScaling2 = self.getFluxErrScaling(kernel, aperShape)
595 self.assertFloatsAlmostEqual(fluxErrScaling1, fluxErrScaling2, rtol=1e-4)
597 @lsst.utils.tests.methodParameters(noise=(0.001, 0.01, 0.1))
598 def testMonteCarlo(self, noise, recordId=1, sigmas=[0.7, 1.0, 1.25],
599 scalingFactors=[1.1, 1.15, 1.2, 1.3, 1.4]):
600 """Test GAaP flux uncertainties.
602 This test should demonstate that the estimated flux uncertainties agree
603 with those from Monte Carlo simulations.
605 Parameters
606 ----------
607 noise : `float`
608 The RMS value of the Gaussian noise field divided by the total flux
609 of the source.
610 recordId : `int`, optional
611 The source Id in the test dataset to measure.
612 sigmas : `list` [`float`], optional
613 The list of sigmas (in pixels) to construct the `GaapFluxConfig`.
614 scalingFactors : `list` [`float`], optional
615 The list of scaling factors to construct the `GaapFluxConfig`.
616 """
617 gaapConfig = lsst.meas.extensions.gaap.SingleFrameGaapFluxConfig(sigmas=sigmas,
618 scalingFactors=scalingFactors)
619 gaapConfig.scaleByFwhm = True
620 gaapConfig.doPsfPhotometry = True
621 gaapConfig.doOptimalPhotometry = True
623 algorithm, schema = self.makeAlgorithm(gaapConfig)
624 # Make a noiseless exposure and keep measurement record for reference
625 exposure, catalog = self.dataset.realize(0.0, schema)
626 if gaapConfig.doOptimalPhotometry:
627 self.recordPsfShape(catalog)
628 recordNoiseless = catalog[recordId]
629 totalFlux = recordNoiseless["truth_instFlux"]
630 algorithm.measure(recordNoiseless, exposure)
632 nSamples = 1024
633 catalog = afwTable.SourceCatalog(schema)
634 for repeat in range(nSamples):
635 exposure, cat = self.dataset.realize(noise*totalFlux, schema, randomSeed=repeat)
636 if gaapConfig.doOptimalPhotometry:
637 self.recordPsfShape(cat)
638 record = cat[recordId]
639 algorithm.measure(record, exposure)
640 catalog.append(record)
642 catalog = catalog.copy(deep=True)
643 for baseName in gaapConfig.getAllGaapResultNames(name="ext_gaap_GaapFlux"):
644 instFluxKey = schema.join(baseName, "instFlux")
645 instFluxErrKey = schema.join(baseName, "instFluxErr")
646 instFluxMean = catalog[instFluxKey].mean()
647 instFluxErrMean = catalog[instFluxErrKey].mean()
648 instFluxStdDev = catalog[instFluxKey].std()
650 # GAaP fluxes are not meant to be total fluxes.
651 # We compare the mean of the noisy measurements to its
652 # corresponding noiseless measurement instead of the true value
653 instFlux = recordNoiseless[instFluxKey]
654 self.assertFloatsAlmostEqual(instFluxErrMean, instFluxStdDev, rtol=0.02)
655 self.assertLess(abs(instFluxMean - instFlux), 2.0*instFluxErrMean/nSamples**0.5)
658class TestMemory(lsst.utils.tests.MemoryTestCase):
659 pass
662def setup_module(module, backend="virtualDevice"):
663 lsst.utils.tests.init()
664 try:
665 afwDisplay.setDefaultBackend(backend)
666 except Exception:
667 print("Unable to configure display backend: %s" % backend)
670if __name__ == "__main__": 670 ↛ 671line 670 didn't jump to line 671, because the condition on line 670 was never true
671 import sys
673 from argparse import ArgumentParser
674 parser = ArgumentParser()
675 parser.add_argument('--backend', type=str, default="virtualDevice",
676 help="The backend to use, e.g. 'ds9'. Be sure to 'setup display_<backend>'")
677 args = parser.parse_args()
679 setup_module(sys.modules[__name__], backend=args.backend)
680 unittest.main()