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

# 

# LSST Data Management System 

# 

# Copyright 2008-2016 AURA/LSST. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# 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 LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <https://www.lsstcorp.org/LegalNotices/>. 

# 

import unittest 

import os 

import numpy 

 

import lsst.utils.tests 

import lsst.meas.modelfit 

 

 

class SemiEmpiricalPriorTestCase(lsst.utils.tests.TestCase): 

 

NUM_DIFF_STEP = 1E-4 

 

def setUp(self): 

# a prior with broad ramps and non-zero slope; broad ramps makes evaluating numerical 

# derivatives easier, and we want to do that to check the analytic ones 

numpy.random.seed(500) 

self.ctrl = lsst.meas.modelfit.SemiEmpiricalPrior.Control() 

self.ctrl.ellipticityCore = 4.0 

self.ctrl.ellipticitySigma = 10.0 

self.ctrl.logRadiusMinOuter = self.ctrl.logRadiusMinInner - 8.0 

self.ctrl.logRadiusMu = 2.0 

self.ctrl.logRadiusSigma = 5.0 

self.ctrl.logRadiusNu = 2.0 

self.prior = lsst.meas.modelfit.SemiEmpiricalPrior(self.ctrl) 

self.amplitudes = numpy.array([1.0], dtype=lsst.meas.modelfit.Scalar) 

dtype = numpy.dtype([("eta1", float), ("eta2", float), ("lnR", float), ("p", float), 

("d_eta1", float), ("d_eta2", float), ("d_lnR", float), 

("d2_eta1_eta1", float), ("d2_eta1_eta2", float), 

("d2_eta1_lnR", float), ("d2_eta2_eta2", float), 

("d2_eta2_lnR", float), ("d2_lnR_lnR", float)]) 

self.data = numpy.loadtxt(os.path.join(os.path.dirname( 

os.path.realpath(__file__)), "data", "SEP.txt"), dtype=dtype) 

 

def tearDown(self): 

del self.prior 

del self.amplitudes 

 

def testEvaluate(self): 

for row in self.data: 

p = self.prior.evaluate(numpy.array([row["eta1"], row["eta2"], row["lnR"]]), self.amplitudes) 

self.assertFloatsAlmostEqual(row["p"], p) 

 

def testGradient(self): 

for row in self.data: 

grad = numpy.zeros(4, dtype=float) 

hess = numpy.zeros((4, 4), dtype=float) 

self.prior.evaluateDerivatives( 

numpy.array([row["eta1"], row["eta2"], row["lnR"]]), 

self.amplitudes, 

grad[:3], grad[3:], 

hess[:3, :3], hess[3:, 3:], hess[:3, 3:] 

) 

self.assertFloatsAlmostEqual(row["d_eta1"], grad[0]) 

self.assertFloatsAlmostEqual(row["d_eta2"], grad[1]) 

self.assertFloatsAlmostEqual(row["d_lnR"], grad[2]) 

 

def testHessian(self): 

for row in self.data: 

grad = numpy.zeros(4, dtype=float) 

hess = numpy.zeros((4, 4), dtype=float) 

self.prior.evaluateDerivatives( 

numpy.array([row["eta1"], row["eta2"], row["lnR"]]), 

self.amplitudes, 

grad[:3], grad[3:], 

hess[:3, :3], hess[3:, 3:], hess[:3, 3:] 

) 

self.assertFloatsAlmostEqual(row["d2_eta1_eta1"], hess[0, 0]) 

self.assertFloatsAlmostEqual(row["d2_eta1_eta2"], hess[0, 1]) 

self.assertFloatsAlmostEqual(row["d2_eta1_lnR"], hess[0, 2]) 

self.assertFloatsAlmostEqual(row["d2_eta2_eta2"], hess[1, 1]) 

self.assertFloatsAlmostEqual(row["d2_eta2_lnR"], hess[1, 2]) 

self.assertFloatsAlmostEqual(row["d2_lnR_lnR"], hess[2, 2]) 

 

def evaluatePrior(self, eta1, eta2, lnR): 

b = numpy.broadcast(eta1, eta2, lnR) 

p = numpy.zeros(b.shape, dtype=lsst.meas.modelfit.Scalar) 

for i, (eta1i, eta2i, lnRi) in enumerate(b): 

p.flat[i] = self.prior.evaluate(numpy.array([eta1i, eta2i, lnRi]), self.amplitudes) 

return p 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

112 ↛ 113line 112 didn't jump to line 113, because the condition on line 112 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()