[d00f8ff] | 1 | """ |
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| 2 | Unit tests for data manipulations |
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| 3 | """ |
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| 4 | |
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| 5 | |
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| 6 | import unittest |
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| 7 | import numpy, math |
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| 8 | from DataLoader.loader import Loader |
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| 9 | from DataLoader.data_info import Data1D, Data2D |
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[4fe4394] | 10 | from DataLoader.qsmearing import SlitSmearer, QSmearer, smear_selection |
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[d00f8ff] | 11 | |
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| 12 | import os.path |
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| 13 | |
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| 14 | class smear_tests(unittest.TestCase): |
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| 15 | |
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| 16 | def setUp(self): |
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| 17 | self.data = Loader().load("cansas1d_slit.xml") |
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| 18 | |
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| 19 | x = 0.001*numpy.arange(1,11) |
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| 20 | y = 12.0-numpy.arange(1,11) |
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| 21 | dxl = 0.00*numpy.ones(10) |
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| 22 | dxw = 0.00*numpy.ones(10) |
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[4fe4394] | 23 | dx = 0.00*numpy.ones(10) |
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[d00f8ff] | 24 | |
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[4fe4394] | 25 | self.data.dx = dx |
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[d00f8ff] | 26 | self.data.x = x |
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| 27 | self.data.y = y |
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| 28 | self.data.dxl = dxl |
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| 29 | self.data.dxw = dxw |
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| 30 | |
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| 31 | def test_slit(self): |
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| 32 | """ |
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| 33 | Test identity smearing |
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| 34 | """ |
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| 35 | # Create smearer for our data |
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| 36 | s = SlitSmearer(self.data) |
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| 37 | |
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| 38 | input = 12.0-numpy.arange(1,11) |
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| 39 | output = s(input) |
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| 40 | for i in range(len(input)): |
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| 41 | self.assertEquals(input[i], output[i]) |
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| 42 | |
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| 43 | def test_slit2(self): |
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| 44 | """ |
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| 45 | Test basic smearing |
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| 46 | """ |
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| 47 | dxl = 0.005*numpy.ones(10) |
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| 48 | dxw = 0.0*numpy.ones(10) |
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| 49 | self.data.dxl = dxl |
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| 50 | self.data.dxw = dxw |
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| 51 | # Create smearer for our data |
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| 52 | s = SlitSmearer(self.data) |
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| 53 | |
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| 54 | input = 12.0-numpy.arange(1,11) |
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| 55 | output = s(input) |
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[5859862] | 56 | # The following commented line was the correct output for even bins [see smearer.cpp for details] |
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| 57 | #answer = [ 9.666, 9.056, 8.329, 7.494, 6.642, 5.721, 4.774, 3.824, 2.871, 2. ] |
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| 58 | answer = [ 9.2302, 8.6806, 7.9533, 7.1673, 6.2889, 5.4, 4.5028, 3.5744, 2.6083, 2. ] |
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[d00f8ff] | 59 | for i in range(len(input)): |
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[a3f8d58] | 60 | self.assertAlmostEqual(answer[i], output[i], 2) |
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[d00f8ff] | 61 | |
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| 62 | def test_q(self): |
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| 63 | """ |
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| 64 | Test identity resolution smearing |
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| 65 | """ |
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| 66 | # Create smearer for our data |
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| 67 | s = QSmearer(self.data) |
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| 68 | |
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| 69 | input = 12.0-numpy.arange(1,11) |
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| 70 | output = s(input) |
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| 71 | for i in range(len(input)): |
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[a3f8d58] | 72 | self.assertAlmostEquals(input[i], output[i], 5) |
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[d00f8ff] | 73 | |
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| 74 | def test_q2(self): |
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| 75 | """ |
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| 76 | Test basic smearing |
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| 77 | """ |
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| 78 | dx = 0.001*numpy.ones(10) |
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| 79 | self.data.dx = dx |
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| 80 | |
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| 81 | # Create smearer for our data |
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| 82 | s = QSmearer(self.data) |
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| 83 | |
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| 84 | input = 12.0-numpy.arange(1,11) |
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| 85 | output = s(input) |
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| 86 | |
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| 87 | answer = [ 10.44785079, 9.84991299, 8.98101708, |
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| 88 | 7.99906585, 6.99998311, 6.00001689, |
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| 89 | 5.00093415, 4.01898292, 3.15008701, 2.55214921] |
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| 90 | for i in range(len(input)): |
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| 91 | self.assertAlmostEqual(answer[i], output[i], 5) |
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| 92 | |
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| 93 | |
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| 94 | if __name__ == '__main__': |
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| 95 | unittest.main() |
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| 96 | |
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