Coverage for tests/test_dcr_assemble_coadd.py: 19%

1# This file is part of drp_tasks. 

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/>. 

21 

22import unittest 

23 

24import lsst.utils.tests 

25from lsst.drp.tasks.dcr_assemble_coadd import DcrAssembleCoaddConfig, DcrAssembleCoaddTask 

26 

27 

28class DcrAssembleCoaddCalculateGainTestCase(lsst.utils.tests.TestCase): 

29 """Tests of dcrAssembleCoaddTask.calculateGain().""" 

30 

31 def setUp(self): 

32 self.baseGain = 0.5 

33 self.gainList = [self.baseGain, self.baseGain] 

34 self.convergenceList = [0.2] 

35 # Calculate the convergence we would expect if the model was converging 

36 # perfectly, so that the improvement is limited only by our 

37 # conservative gain. 

38 for i in range(2): 

39 self.convergenceList.append(self.convergenceList[i] / (self.baseGain + 1)) 

40 self.nextGain = (1 + self.baseGain) / 2 

41 

42 self.config = DcrAssembleCoaddConfig() 

43 self.config.effectiveWavelength = 500.0 

44 self.config.bandwidth = 100.0 

45 self.task = DcrAssembleCoaddTask(config=self.config) 

46 

47 def testUnbalancedLists(self): 

48 gainList = [1, 2, 3, 4] 

49 convergenceList = [1, 2] 

50 with self.assertRaises(ValueError): 

51 self.task.calculateGain(convergenceList, gainList) 

52 

53 def testNoProgressiveGain(self): 

54 self.config.useProgressiveGain = False 

55 self.config.baseGain = self.baseGain 

56 expectGain = self.baseGain 

57 expectGainList = self.gainList + [expectGain] 

58 result = self.task.calculateGain(self.convergenceList, self.gainList) 

59 self.assertEqual(result, expectGain) 

60 self.assertEqual(self.gainList, expectGainList) 

61 

62 def testBaseGainNone(self): 

63 """If baseGain is None, gain is calculated from the default values.""" 

64 self.config.useProgressiveGain = False 

65 expectGain = 1 / (self.config.dcrNumSubfilters - 1) 

66 expectGainList = self.gainList + [expectGain] 

67 result = self.task.calculateGain(self.convergenceList, self.gainList) 

68 self.assertEqual(result, expectGain) 

69 self.assertEqual(self.gainList, expectGainList) 

70 

71 def testProgressiveFirstStep(self): 

72 """The first and second steps always return baseGain.""" 

73 convergenceList = self.convergenceList[:1] 

74 gainList = [] 

75 self.config.baseGain = self.baseGain 

76 expectGain = self.baseGain 

77 expectGainList = [expectGain] 

78 result = self.task.calculateGain(convergenceList, gainList) 

79 self.assertEqual(result, expectGain) 

80 self.assertEqual(gainList, expectGainList) 

81 

82 def testProgressiveSecondStep(self): 

83 """The first and second steps always return baseGain.""" 

84 convergenceList = self.convergenceList[:2] 

85 gainList = self.gainList[:1] 

86 self.config.baseGain = self.baseGain 

87 expectGain = self.baseGain 

88 expectGainList = gainList + [expectGain] 

89 result = self.task.calculateGain(convergenceList, gainList) 

90 self.assertEqual(result, expectGain) 

91 self.assertEqual(gainList, expectGainList) 

92 

93 def testProgressiveGain(self): 

94 """Test that gain follows the "perfect" situation defined in setUp.""" 

95 self.config.baseGain = self.baseGain 

96 expectGain = self.nextGain 

97 expectGainList = self.gainList + [expectGain] 

98 result = self.task.calculateGain(self.convergenceList, self.gainList) 

99 self.assertFloatsAlmostEqual(result, expectGain) 

100 self.assertEqual(self.gainList, expectGainList) 

101 

102 def testProgressiveGainBadFit(self): 

103 """Test that gain is reduced if the predicted convergence does not 

104 match the measured convergence (in this case, converging too quickly). 

105 """ 

106 wrongGain = 1.0 

107 gainList = [self.baseGain, self.baseGain] 

108 convergenceList = [0.2] 

109 for i in range(2): 

110 convergenceList.append(convergenceList[i] / (wrongGain + 1)) 

111 # The below math is a simplified version of the full algorithm, 

112 # assuming the predicted convergence is zero. 

113 # Note that in this case, nextGain is smaller than wrongGain. 

114 nextGain = (self.baseGain + (1 + self.baseGain) / (1 + wrongGain)) / 2 

115 

116 self.config.baseGain = self.baseGain 

117 expectGain = nextGain 

118 expectGainList = self.gainList + [expectGain] 

119 result = self.task.calculateGain(convergenceList, gainList) 

120 self.assertFloatsAlmostEqual(result, nextGain) 

121 self.assertEqual(gainList, expectGainList) 

122 

123 

124def setup_module(module): 

125 lsst.utils.tests.init() 

126 

127 

128class MatchMemoryTestCase(lsst.utils.tests.MemoryTestCase): 

129 pass 

130 

131 

132if __name__ == "__main__": 132 ↛ 133line 132 didn't jump to line 133, because the condition on line 132 was never true

133 lsst.utils.tests.init() 

134 unittest.main()