def test_Q(self):
ConvertWANDSCDtoQTest_out=ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',BinningDim0='-8.08,8.08,101',
BinningDim1='-0.88,0.88,11',BinningDim2='-8.08,8.08,101',NormaliseBy='None')
self.assertTrue(ConvertWANDSCDtoQTest_out)
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 8.97233331213612)
self.assertAlmostEqual(np.nanargmax(s), 22780)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumDims(), 3)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNPoints(), 112211)
d0 = ConvertWANDSCDtoQTest_out.getDimension(0)
self.assertEqual(d0.name, 'Q_sample_x')
self.assertEqual(d0.getNBins(), 101)
self.assertAlmostEquals(d0.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d0.getMaximum(), 8.08, 5)
d1 = ConvertWANDSCDtoQTest_out.getDimension(1)
self.assertEqual(d1.name, 'Q_sample_y')
self.assertEqual(d1.getNBins(), 11)
self.assertAlmostEquals(d1.getMinimum(), -0.88, 5)
self.assertAlmostEquals(d1.getMaximum(), 0.88, 5)
d2 = ConvertWANDSCDtoQTest_out.getDimension(2)
self.assertEqual(d2.name, 'Q_sample_z')
self.assertEqual(d2.getNBins(), 101)
self.assertAlmostEquals(d2.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d2.getMaximum(), 8.08, 5)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumExperimentInfo(), 1)
ConvertWANDSCDtoQTest_out.delete()
def test_Q(self):
ConvertWANDSCDtoQTest_out=ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',BinningDim0='-8.08,8.08,101',
BinningDim1='-0.88,0.88,11',BinningDim2='-8.08,8.08,101',NormaliseBy='None')
self.assertTrue(ConvertWANDSCDtoQTest_out)
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 8.97233331213612)
self.assertAlmostEqual(np.nanargmax(s), 22780)
self.assertEquals(ConvertWANDSCDtoQTest_out.getNumDims(), 3)
self.assertEquals(ConvertWANDSCDtoQTest_out.getNPoints(), 112211)
d0 = ConvertWANDSCDtoQTest_out.getDimension(0)
self.assertEquals(d0.name, 'Q_sample_x')
self.assertEquals(d0.getNBins(), 101)
self.assertAlmostEquals(d0.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d0.getMaximum(), 8.08, 5)
d1 = ConvertWANDSCDtoQTest_out.getDimension(1)
self.assertEquals(d1.name, 'Q_sample_y')
self.assertEquals(d1.getNBins(), 11)
self.assertAlmostEquals(d1.getMinimum(), -0.88, 5)
self.assertAlmostEquals(d1.getMaximum(), 0.88, 5)
d2 = ConvertWANDSCDtoQTest_out.getDimension(2)
self.assertEquals(d2.name, 'Q_sample_z')
self.assertEquals(d2.getNBins(), 101)
self.assertAlmostEquals(d2.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d2.getMaximum(), 8.08, 5)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumExperimentInfo(), 1)
ConvertWANDSCDtoQTest_out.delete()
def test_Q_norm(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',NormalisationWorkspace='ConvertWANDSCDtoQTest_norm',
BinningDim0='-8.08,8.08,101',BinningDim1='-0.88,0.88,11',BinningDim2='-8.08,8.08,101')
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 7.476944426780101)
self.assertAlmostEqual(np.nanargmax(s), 22780)
ConvertWANDSCDtoQTest_out.delete()
def test_Q_norm(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',NormalisationWorkspace='ConvertWANDSCDtoQTest_norm',
BinningDim0='-8.08,8.08,101',BinningDim1='-0.88,0.88,11',BinningDim2='-8.08,8.08,101')
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 7.476944426780101)
self.assertAlmostEqual(np.nanargmax(s), 22780)
ConvertWANDSCDtoQTest_out.delete()
def test_HKL_norm_and_KeepTemporary(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ(
'ConvertWANDSCDtoQTest_data',
NormalisationWorkspace='ConvertWANDSCDtoQTest_norm',
Frame='HKL',
KeepTemporaryWorkspaces=True,
BinningDim0='-8.08,8.08,101',
BinningDim1='-8.08,8.08,101',
BinningDim2='-8.08,8.08,101',
Uproj='1,1,0',
Vproj='1,-1,0',
Wproj='0,0,1')
self.assertTrue(ConvertWANDSCDtoQTest_out)
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out'))
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out_data'))
self.assertTrue(
mtd.doesExist('ConvertWANDSCDtoQTest_out_normalization'))
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 4.646855396509936)
self.assertAlmostEqual(np.nanargmax(s), 443011)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumDims(), 3)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNPoints(), 101**3)
d0 = ConvertWANDSCDtoQTest_out.getDimension(0)
self.assertEqual(d0.name, '[H,H,0]')
self.assertEqual(d0.getNBins(), 101)
self.assertAlmostEquals(d0.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d0.getMaximum(), 8.08, 5)
d1 = ConvertWANDSCDtoQTest_out.getDimension(1)
self.assertEqual(d1.name, '[H,-H,0]')
self.assertEqual(d1.getNBins(), 101)
self.assertAlmostEquals(d1.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d1.getMaximum(), 8.08, 5)
d2 = ConvertWANDSCDtoQTest_out.getDimension(2)
self.assertEqual(d2.name, '[0,0,L]')
self.assertEqual(d2.getNBins(), 101)
self.assertAlmostEquals(d2.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d2.getMaximum(), 8.08, 5)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumExperimentInfo(), 1)
ConvertWANDSCDtoQTest_out.delete()
def test_HKL_norm_and_KeepTemporary(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ('ConvertWANDSCDtoQTest_data',NormalisationWorkspace='ConvertWANDSCDtoQTest_norm',
Frame='HKL',KeepTemporaryWorkspaces=True,BinningDim0='-8.08,8.08,101',
BinningDim1='-8.08,8.08,101',BinningDim2='-8.08,8.08,101',
Uproj='1,1,0',Vproj='1,-1,0',Wproj='0,0,1')
self.assertTrue(ConvertWANDSCDtoQTest_out)
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out'))
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out_data'))
self.assertTrue(mtd.doesExist('ConvertWANDSCDtoQTest_out_normalization'))
s = ConvertWANDSCDtoQTest_out.getSignalArray()
self.assertAlmostEqual(np.nanmax(s), 4.646855396509936)
self.assertAlmostEqual(np.nanargmax(s), 443011)
self.assertEquals(ConvertWANDSCDtoQTest_out.getNumDims(), 3)
self.assertEquals(ConvertWANDSCDtoQTest_out.getNPoints(), 101**3)
d0 = ConvertWANDSCDtoQTest_out.getDimension(0)
self.assertEquals(d0.name, '[H,H,0]')
self.assertEquals(d0.getNBins(), 101)
self.assertAlmostEquals(d0.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d0.getMaximum(), 8.08, 5)
d1 = ConvertWANDSCDtoQTest_out.getDimension(1)
self.assertEquals(d1.name, '[H,-H,0]')
self.assertEquals(d1.getNBins(), 101)
self.assertAlmostEquals(d1.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d1.getMaximum(), 8.08, 5)
d2 = ConvertWANDSCDtoQTest_out.getDimension(2)
self.assertEquals(d2.name, '[0,0,L]')
self.assertEquals(d2.getNBins(), 101)
self.assertAlmostEquals(d2.getMinimum(), -8.08, 5)
self.assertAlmostEquals(d2.getMaximum(), 8.08, 5)
self.assertEqual(ConvertWANDSCDtoQTest_out.getNumExperimentInfo(), 1)
ConvertWANDSCDtoQTest_out.delete()
def test_COP(self):
ConvertWANDSCDtoQTest_out = ConvertWANDSCDtoQ(
'ConvertWANDSCDtoQTest_data',
BinningDim0='-8.08,8.08,101',
BinningDim1='-1.68,1.68,21',
BinningDim2='-8.08,8.08,101',
NormaliseBy='None')
ConvertWANDSCDtoQTest_cop = ConvertWANDSCDtoQ(
'ConvertWANDSCDtoQTest_data',
BinningDim0='-8.08,8.08,101',
BinningDim1='-1.68,1.68,21',
BinningDim2='-8.08,8.08,101',
NormaliseBy='None',
ObliquityParallaxCoefficient=1.5)
self.assertTrue(ConvertWANDSCDtoQTest_out)
self.assertTrue(ConvertWANDSCDtoQTest_cop)
Test_out = ConvertWANDSCDtoQTest_out.getSignalArray().copy()
Test_cop = ConvertWANDSCDtoQTest_cop.getSignalArray().copy()
x, y, z = np.meshgrid(np.linspace(-8, 8, 101),
np.linspace(-1.6, 1.6, 21),
np.linspace(-8, 8, 101),
indexing='ij')
Test_out_max_Qy = y[~np.isnan(Test_out)].max()
Test_cop_max_Qy = y[~np.isnan(Test_cop)].max()
# Test whether Qy is scaled by ObliquityParallaxCoefficient correctly
proportion = Test_cop_max_Qy / Test_out_max_Qy
self.assertAlmostEquals(proportion, 1.5, 5)
ConvertWANDSCDtoQTest_out.delete()
ConvertWANDSCDtoQTest_cop.delete()