def test_with_data_from_other_workspace(self):
wsname = 'LOQ'
x1 = np.array([1., 2., 3., 4.])
y1 = np.array([[1., 2., 3.], [4., 5., 6.]])
e1 = np.sqrt(y1)
loq = CreateWorkspace(DataX=x1,
DataY=y1,
DataE=e1,
NSpec=2,
UnitX='Wavelength')
x2 = loq.extractX()
y2 = loq.extractY()
e2 = loq.extractE()
wksp = CreateWorkspace(DataX=x2,
DataY=y2,
DataE=e2,
NSpec=2,
UnitX='Wavelength')
self.assertTrue(isinstance(wksp, MatrixWorkspace))
self.assertEqual(wksp.getNumberHistograms(), 2)
for i in [0, 1]:
for j in range(len(y2[0])):
self.assertEqual(wksp.readY(i)[j], loq.readY(i)[j])
self.assertEqual(wksp.readE(i)[j], loq.readE(i)[j])
self.assertEqual(wksp.readX(i)[j], loq.readX(i)[j])
# Last X value
self.assertEqual(
wksp.readX(i)[len(x2) - 1],
loq.readX(i)[len(x2) - 1])
AnalysisDataService.remove("wksp")
def test_create_with_2D_numpy_array(self):
x = np.array([1.,2.,3.,4.])
y = np.array([[1.,2.,3.],[4.,5.,6.]])
e = np.sqrt(y)
wksp = CreateWorkspace(DataX=x, DataY=y,DataE=e,NSpec=2,UnitX='TOF')
self.assertTrue(isinstance(wksp, MatrixWorkspace))
self.assertEquals(wksp.getNumberHistograms(), 2)
for i in [0,1]:
for j in range(len(y[0])):
self.assertEquals(wksp.readY(i)[j], y[i][j])
self.assertEquals(wksp.readE(i)[j], e[i][j])
self.assertEquals(wksp.readX(i)[j], x[j])
# Last X value
self.assertEquals(wksp.readX(i)[len(x)-1], x[len(x)-1])
AnalysisDataService.remove("wksp")
def test_create_with_2D_numpy_array(self):
x = np.array([1., 2., 3., 4.])
y = np.array([[1., 2., 3.], [4., 5., 6.]])
e = np.sqrt(y)
wksp = CreateWorkspace(DataX=x, DataY=y, DataE=e, NSpec=2, UnitX='TOF')
self.assertTrue(isinstance(wksp, MatrixWorkspace))
self.assertEqual(wksp.getNumberHistograms(), 2)
for i in [0, 1]:
for j in range(len(y[0])):
self.assertEqual(wksp.readY(i)[j], y[i][j])
self.assertEqual(wksp.readE(i)[j], e[i][j])
self.assertEqual(wksp.readX(i)[j], x[j])
# Last X value
self.assertEqual(wksp.readX(i)[len(x) - 1], x[len(x) - 1])
AnalysisDataService.remove("wksp")
def test_create_with_1D_numpy_array(self):
x = np.array([1.,2.,3.,4.])
y = np.array([1.,2.,3.])
e = np.sqrt(np.array([1.,2.,3.]))
wksp = CreateWorkspace(DataX=x, DataY=y,DataE=e,NSpec=1,UnitX='TOF')
self.assertTrue(isinstance(wksp, MatrixWorkspace))
self.assertEquals(wksp.getNumberHistograms(), 1)
self.assertEquals(len(wksp.readY(0)), len(y))
self.assertEquals(len(wksp.readX(0)), len(x))
self.assertEquals(len(wksp.readE(0)), len(e))
for index in range(len(y)):
self.assertEquals(wksp.readY(0)[index], y[index])
self.assertEquals(wksp.readE(0)[index], e[index])
self.assertEquals(wksp.readX(0)[index], x[index])
# Last X value
self.assertEquals(wksp.readX(0)[len(x)-1], x[len(x)-1])
AnalysisDataService.remove("wksp")
def test_create_with_1D_numpy_array(self):
x = np.array([1., 2., 3., 4.])
y = np.array([1., 2., 3.])
e = np.sqrt(np.array([1., 2., 3.]))
wksp = CreateWorkspace(DataX=x, DataY=y, DataE=e, NSpec=1, UnitX='TOF')
self.assertTrue(isinstance(wksp, MatrixWorkspace))
self.assertEqual(wksp.getNumberHistograms(), 1)
self.assertEqual(len(wksp.readY(0)), len(y))
self.assertEqual(len(wksp.readX(0)), len(x))
self.assertEqual(len(wksp.readE(0)), len(e))
for index in range(len(y)):
self.assertEqual(wksp.readY(0)[index], y[index])
self.assertEqual(wksp.readE(0)[index], e[index])
self.assertEqual(wksp.readX(0)[index], x[index])
# Last X value
self.assertEqual(wksp.readX(0)[len(x) - 1], x[len(x) - 1])
AnalysisDataService.remove("wksp")
def test_with_data_from_other_workspace(self):
wsname = 'LOQ'
alg = run_algorithm('Load', Filename='LOQ48127.raw', OutputWorkspace=wsname, SpectrumMax=2, child=True)
loq = alg.getProperty("OutputWorkspace").value
x = loq.extractX()
y = loq.extractY()
e = loq.extractE()
wksp = CreateWorkspace(DataX=x, DataY=y,DataE=e,NSpec=2,UnitX='Wavelength')
self.assertTrue(isinstance(wksp, MatrixWorkspace))
self.assertEquals(wksp.getNumberHistograms(), 2)
for i in [0,1]:
for j in range(len(y[0])):
self.assertEquals(wksp.readY(i)[j], loq.readY(i)[j])
self.assertEquals(wksp.readE(i)[j], loq.readE(i)[j])
self.assertEquals(wksp.readX(i)[j], loq.readX(i)[j])
# Last X value
self.assertEquals(wksp.readX(i)[len(x)-1], loq.readX(i)[len(x)-1])
AnalysisDataService.remove("wksp")
def test_with_data_from_other_workspace(self):
wsname = 'LOQ'
x1 = np.array([1.,2.,3.,4.])
y1 = np.array([[1.,2.,3.],[4.,5.,6.]])
e1 = np.sqrt(y1)
loq = CreateWorkspace(DataX=x1, DataY=y1,DataE=e1,NSpec=2,UnitX='Wavelength')
x2 = loq.extractX()
y2 = loq.extractY()
e2 = loq.extractE()
wksp = CreateWorkspace(DataX=x2, DataY=y2,DataE=e2,NSpec=2,UnitX='Wavelength')
self.assertTrue(isinstance(wksp, MatrixWorkspace))
self.assertEquals(wksp.getNumberHistograms(), 2)
for i in [0,1]:
for j in range(len(y2[0])):
self.assertEquals(wksp.readY(i)[j], loq.readY(i)[j])
self.assertEquals(wksp.readE(i)[j], loq.readE(i)[j])
self.assertEquals(wksp.readX(i)[j], loq.readX(i)[j])
# Last X value
self.assertEquals(wksp.readX(i)[len(x2)-1], loq.readX(i)[len(x2)-1])
AnalysisDataService.remove("wksp")
def test_container_input_workspace_not_unintentionally_rebinned(self):
xs = numpy.array([0.0, 1.0, 0.0, 1.1])
ys = numpy.array([2.2, 3.3])
sample_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
ys = numpy.array([0.11, 0.22])
container_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
corrected = ApplyPaalmanPingsCorrection(SampleWorkspace=sample_1,
CanWorkspace=container_1)
numHisto = container_1.getNumberHistograms()
for i in range(numHisto):
container_xs = container_1.readX(i)
for j in range(len(container_xs)):
self.assertEqual(container_xs[j], xs[i * numHisto + j])
DeleteWorkspace(sample_1)
DeleteWorkspace(container_1)
DeleteWorkspace(corrected)
def test_function_deriv_with_1DHistogram_Domain(self):
g = FunctionWrapper("Gaussian", Height=7.5, Sigma=1.2, PeakCentre=10)
ws = CreateWorkspace(DataX=[0, 1, 2, 3, 4], DataY=[5, 5, 5, 5])
dom = FunctionDomain1DHistogram(ws.readX(0))
out = g.functionDeriv(dom)
self.assertAlmostEqual(out.get(0, 0), 0.00000000000009592326932761353)
self.assertAlmostEqual(out.get(0, 1), -0.000000000004570209530946241)
self.assertAlmostEqual(out.get(0, 2), -0.00000000003012829985493681)
self.assertAlmostEqual(out.get(1, 0), 0.00000000003925992864139971)
self.assertAlmostEqual(out.get(1, 1), -0.0000000016706471246441646)
self.assertAlmostEqual(out.get(1, 2), -0.000000009831636305079881)
self.assertAlmostEqual(out.get(2, 0), 0.000000008131882500705956)
self.assertAlmostEqual(out.get(2, 1), -0.0000003045374795085354)
self.assertAlmostEqual(out.get(2, 2), -0.0000015775749015123351)
def test_container_input_workspace_not_unintentionally_rebinned(self):
xs = numpy.array([0.0, 1.0, 0.0, 1.1])
ys = numpy.array([2.2, 3.3])
sample_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
ys = numpy.array([0.11, 0.22])
container_1 = CreateWorkspace(DataX=xs, DataY=ys, NSpec=2,
UnitX='Wavelength')
corrected = ApplyPaalmanPingsCorrection(SampleWorkspace=sample_1,
CanWorkspace=container_1)
numHisto = container_1.getNumberHistograms()
for i in range(numHisto):
container_xs = container_1.readX(i)
for j in range(len(container_xs)):
self.assertEqual(container_xs[j], xs[i * numHisto + j])
DeleteWorkspace(sample_1)
DeleteWorkspace(container_1)
DeleteWorkspace(corrected)
def test_function_deriv_with_1DVector_Domain(self):
g = FunctionWrapper("Gaussian", Height=7.5, Sigma=1.2, PeakCentre=10)
ws = CreateWorkspace(DataX=[0, 1, 2, 3, 4], DataY=[5, 5, 5, 5])
dom = FunctionDomain1DVector(ws.readX(0))
out = g.functionDeriv(dom)
self.assertAlmostEqual(out.get(0, 0),
0.0000000000000008323969676981107)
self.assertAlmostEqual(out.get(0, 1),
-0.000000000000043354008734276606)
self.assertAlmostEqual(out.get(0, 2), -0.00000000000031214886288679154)
self.assertAlmostEqual(out.get(1, 0), 0.0000000000006101936677605303)
self.assertAlmostEqual(out.get(1, 1), -0.00000000002860282817627486)
self.assertAlmostEqual(out.get(1, 2), -0.00000000018534632658226106)
self.assertAlmostEqual(out.get(2, 0), 0.00000000022336314362031582)
self.assertAlmostEqual(out.get(2, 1), -0.000000009306797650846493)
self.assertAlmostEqual(out.get(2, 2), -0.0000000536071544688758)
