Hide keyboard shortcuts

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

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

# This file is part of meas_base. 

# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (https://www.lsst.org). 

# See the COPYRIGHT file at the top-level directory of this distribution 

# for details of code ownership. 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program is distributed in the hope that it will be useful, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the GNU General Public License 

# along with this program. If not, see <https://www.gnu.org/licenses/>. 

 

import unittest 

 

import numpy as np 

 

import lsst.geom 

import lsst.afw.geom 

import lsst.meas.base 

import lsst.utils.tests 

from lsst.meas.base.tests import (AlgorithmTestCase, FluxTransformTestCase, 

SingleFramePluginTransformSetupHelper) 

 

 

class GaussianFluxTestCase(AlgorithmTestCase, lsst.utils.tests.TestCase): 

 

def setUp(self): 

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

lsst.geom.Extent2I(240, 1600)) 

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

# first source is a point 

self.dataset.addSource(100000.0, lsst.geom.Point2D(50.1, 49.8)) 

# second source is extended 

self.dataset.addSource(100000.0, lsst.geom.Point2D(149.9, 50.3), 

lsst.afw.geom.Quadrupole(8, 9, 3)) 

 

def tearDown(self): 

del self.bbox 

del self.dataset 

 

def makeAlgorithm(self, ctrl=None): 

"""Construct an algorithm and return both it and its schema. 

""" 

if ctrl is None: 

ctrl = lsst.meas.base.GaussianFluxControl() 

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

algorithm = lsst.meas.base.GaussianFluxAlgorithm(ctrl, "base_GaussianFlux", schema) 

return algorithm, schema 

 

def testGaussians(self): 

"""Test for correct instFlux given known position and shape. 

""" 

task = self.makeSingleFrameMeasurementTask("base_GaussianFlux") 

# Results are RNG dependent; we choose a seed that is known to pass. 

exposure, catalog = self.dataset.realize(10.0, task.schema, randomSeed=0) 

task.run(catalog, exposure) 

for measRecord in catalog: 

self.assertFloatsAlmostEqual(measRecord.get("base_GaussianFlux_instFlux"), 

measRecord.get("truth_instFlux"), rtol=3E-3) 

 

def testMonteCarlo(self): 

"""Test an ideal simulation, with no noise. 

 

Demonstrate that: 

 

- We get exactly the right answer, and 

- The reported uncertainty agrees with a Monte Carlo test of the noise. 

""" 

algorithm, schema = self.makeAlgorithm() 

# Results are RNG dependent; we choose a seed that is known to pass. 

exposure, catalog = self.dataset.realize(1E-8, schema, randomSeed=1) 

record = catalog[0] 

instFlux = record.get("truth_instFlux") 

algorithm.measure(record, exposure) 

self.assertFloatsAlmostEqual(record.get("base_GaussianFlux_instFlux"), instFlux, rtol=1E-3) 

self.assertLess(record.get("base_GaussianFlux_instFluxErr"), 1E-3) 

for noise in (0.001, 0.01, 0.1): 

instFluxes = [] 

instFluxErrs = [] 

nSamples = 1000 

for repeat in range(nSamples): 

# By using ``repeat`` to seed the RNG, we get results which 

# fall within the tolerances defined below. If we allow this 

# test to be truly random, passing becomes RNG-dependent. 

exposure, catalog = self.dataset.realize(noise*instFlux, schema, randomSeed=repeat) 

record = catalog[1] 

algorithm.measure(record, exposure) 

instFluxes.append(record.get("base_GaussianFlux_instFlux")) 

instFluxErrs.append(record.get("base_GaussianFlux_instFluxErr")) 

instFluxMean = np.mean(instFluxes) 

instFluxErrMean = np.mean(instFluxErrs) 

instFluxStandardDeviation = np.std(instFluxes) 

self.assertFloatsAlmostEqual(instFluxErrMean, instFluxStandardDeviation, rtol=0.10) 

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

 

def testForcedPlugin(self): 

task = self.makeForcedMeasurementTask("base_GaussianFlux") 

# Results of this test are RNG dependent: we choose seeds that are 

# known to pass. 

measWcs = self.dataset.makePerturbedWcs(self.dataset.exposure.getWcs(), randomSeed=2) 

measDataset = self.dataset.transform(measWcs) 

exposure, truthCatalog = measDataset.realize(10.0, measDataset.makeMinimalSchema(), randomSeed=2) 

refWcs = self.dataset.exposure.getWcs() 

refCat = self.dataset.catalog 

measCat = task.generateMeasCat(exposure, refCat, refWcs) 

task.attachTransformedFootprints(measCat, refCat, exposure, refWcs) 

task.run(measCat, exposure, refCat, refWcs) 

for measRecord, truthRecord in zip(measCat, truthCatalog): 

# Centroid tolerances set to ~ single precision epsilon 

self.assertFloatsAlmostEqual(measRecord.get("slot_Centroid_x"), 

truthRecord.get("truth_x"), rtol=1E-7) 

self.assertFloatsAlmostEqual(measRecord.get("slot_Centroid_y"), 

truthRecord.get("truth_y"), rtol=1E-7) 

self.assertFalse(measRecord.get("base_GaussianFlux_flag")) 

# GaussianFlux isn't designed to do a good job in forced mode, 

# because it doesn't account for changes in the PSF (and in fact 

# confuses them with changes in the WCS). Hence, this is really 

# just a regression test, with the initial threshold set to just a 

# bit more than what it was found to be at one point. 

self.assertFloatsAlmostEqual(measRecord.get("base_GaussianFlux_instFlux"), 

truthCatalog.get("truth_instFlux"), rtol=0.3) 

self.assertLess(measRecord.get("base_GaussianFlux_instFluxErr"), 500.0) 

 

 

class GaussianFluxTransformTestCase(FluxTransformTestCase, SingleFramePluginTransformSetupHelper, 

lsst.utils.tests.TestCase): 

controlClass = lsst.meas.base.GaussianFluxControl 

algorithmClass = lsst.meas.base.GaussianFluxAlgorithm 

transformClass = lsst.meas.base.GaussianFluxTransform 

singleFramePlugins = ('base_GaussianFlux',) 

forcedPlugins = ('base_GaussianFlux',) 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

151 ↛ 152line 151 didn't jump to line 152, because the condition on line 151 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()