Coverage for tests/test_GaussianFlux.py: 27%
80 statements
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1# This file is part of meas_base.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://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 GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22import unittest
24import numpy as np
26import lsst.geom
27import lsst.afw.geom
28import lsst.meas.base
29import lsst.utils.tests
30from lsst.meas.base.tests import (AlgorithmTestCase, FluxTransformTestCase,
31 SingleFramePluginTransformSetupHelper)
34class GaussianFluxTestCase(AlgorithmTestCase, lsst.utils.tests.TestCase):
36 def setUp(self):
37 self.bbox = lsst.geom.Box2I(lsst.geom.Point2I(-20, -30),
38 lsst.geom.Extent2I(240, 1600))
39 self.dataset = lsst.meas.base.tests.TestDataset(self.bbox)
40 # first source is a point
41 self.dataset.addSource(100000.0, lsst.geom.Point2D(50.1, 49.8))
42 # second source is extended
43 self.dataset.addSource(100000.0, lsst.geom.Point2D(149.9, 50.3),
44 lsst.afw.geom.Quadrupole(8, 9, 3))
46 def tearDown(self):
47 del self.bbox
48 del self.dataset
50 def makeAlgorithm(self, ctrl=None):
51 """Construct an algorithm and return both it and its schema.
52 """
53 if ctrl is None:
54 ctrl = lsst.meas.base.GaussianFluxControl()
55 schema = lsst.meas.base.tests.TestDataset.makeMinimalSchema()
56 algorithm = lsst.meas.base.GaussianFluxAlgorithm(ctrl, "base_GaussianFlux", schema)
57 return algorithm, schema
59 def testGaussians(self):
60 """Test for correct instFlux given known position and shape.
61 """
62 task = self.makeSingleFrameMeasurementTask("base_GaussianFlux")
63 # Results are RNG dependent; we choose a seed that is known to pass.
64 exposure, catalog = self.dataset.realize(10.0, task.schema, randomSeed=0)
65 task.run(catalog, exposure)
66 for measRecord in catalog:
67 self.assertFloatsAlmostEqual(measRecord.get("base_GaussianFlux_instFlux"),
68 measRecord.get("truth_instFlux"), rtol=3E-3)
70 def testMonteCarlo(self):
71 """Test an ideal simulation, with no noise.
73 Demonstrate that:
75 - We get exactly the right answer, and
76 - The reported uncertainty agrees with a Monte Carlo test of the noise.
77 """
78 algorithm, schema = self.makeAlgorithm()
79 # Results are RNG dependent; we choose a seed that is known to pass.
80 exposure, catalog = self.dataset.realize(1E-8, schema, randomSeed=1)
81 record = catalog[0]
82 instFlux = record.get("truth_instFlux")
83 algorithm.measure(record, exposure)
84 self.assertFloatsAlmostEqual(record.get("base_GaussianFlux_instFlux"), instFlux, rtol=1E-3)
85 self.assertLess(record.get("base_GaussianFlux_instFluxErr"), 1E-3)
86 for noise in (0.001, 0.01, 0.1):
87 instFluxes = []
88 instFluxErrs = []
89 nSamples = 1000
90 for repeat in range(nSamples):
91 # By using ``repeat`` to seed the RNG, we get results which
92 # fall within the tolerances defined below. If we allow this
93 # test to be truly random, passing becomes RNG-dependent.
94 exposure, catalog = self.dataset.realize(noise*instFlux, schema, randomSeed=repeat)
95 record = catalog[1]
96 algorithm.measure(record, exposure)
97 instFluxes.append(record.get("base_GaussianFlux_instFlux"))
98 instFluxErrs.append(record.get("base_GaussianFlux_instFluxErr"))
99 instFluxMean = np.mean(instFluxes)
100 instFluxErrMean = np.mean(instFluxErrs)
101 instFluxStandardDeviation = np.std(instFluxes)
102 self.assertFloatsAlmostEqual(instFluxErrMean, instFluxStandardDeviation, rtol=0.10)
103 self.assertLess(abs(instFluxMean - instFlux), 2.0*instFluxErrMean / nSamples**0.5)
105 def testForcedPlugin(self):
106 task = self.makeForcedMeasurementTask("base_GaussianFlux")
107 # Results of this test are RNG dependent: we choose seeds that are
108 # known to pass.
109 measWcs = self.dataset.makePerturbedWcs(self.dataset.exposure.getWcs(), randomSeed=2)
110 measDataset = self.dataset.transform(measWcs)
111 exposure, truthCatalog = measDataset.realize(10.0, measDataset.makeMinimalSchema(), randomSeed=2)
112 refWcs = self.dataset.exposure.getWcs()
113 refCat = self.dataset.catalog
114 measCat = task.generateMeasCat(exposure, refCat, refWcs)
115 task.attachTransformedFootprints(measCat, refCat, exposure, refWcs)
116 task.run(measCat, exposure, refCat, refWcs)
117 for measRecord, truthRecord in zip(measCat, truthCatalog):
118 # Centroid tolerances set to ~ single precision epsilon
119 self.assertFloatsAlmostEqual(measRecord.get("slot_Centroid_x"),
120 truthRecord.get("truth_x"), rtol=1E-7)
121 self.assertFloatsAlmostEqual(measRecord.get("slot_Centroid_y"),
122 truthRecord.get("truth_y"), rtol=1E-7)
123 self.assertFalse(measRecord.get("base_GaussianFlux_flag"))
124 # GaussianFlux isn't designed to do a good job in forced mode,
125 # because it doesn't account for changes in the PSF (and in fact
126 # confuses them with changes in the WCS). Hence, this is really
127 # just a regression test, with the initial threshold set to just a
128 # bit more than what it was found to be at one point.
129 self.assertFloatsAlmostEqual(measRecord.get("base_GaussianFlux_instFlux"),
130 truthCatalog.get("truth_instFlux"), rtol=0.3)
131 self.assertLess(measRecord.get("base_GaussianFlux_instFluxErr"), 500.0)
134class GaussianFluxTransformTestCase(FluxTransformTestCase, SingleFramePluginTransformSetupHelper,
135 lsst.utils.tests.TestCase):
136 controlClass = lsst.meas.base.GaussianFluxControl
137 algorithmClass = lsst.meas.base.GaussianFluxAlgorithm
138 transformClass = lsst.meas.base.GaussianFluxTransform
139 singleFramePlugins = ('base_GaussianFlux',)
140 forcedPlugins = ('base_GaussianFlux',)
143class TestMemory(lsst.utils.tests.MemoryTestCase):
144 pass
147def setup_module(module):
148 lsst.utils.tests.init()
151if __name__ == "__main__": 151 ↛ 152line 151 didn't jump to line 152, because the condition on line 151 was never true
152 lsst.utils.tests.init()
153 unittest.main()