def test_that_correctly_identifies_normalisation_for_artificial_double_pulse_data(
self):
delta = 0.33
x = np.linspace(0., 15., 100)
x_offset = np.linspace(delta / 2, 15. + delta / 2, 100)
x_offset_neg = np.linspace(-delta / 2, 15. - delta / 2, 100)
testFunction = GausOsc(Frequency=1.5, A=0.22)
y1 = testFunction(x_offset_neg)
y2 = testFunction(x_offset)
N0 = 6.38
y = N0 * (1 + y1 / 2 + y2 / 2)
y_norm = y1 / 2 + y2 / 2
CreateWorkspace(x, y, OutputWorkspace="unnormalised_workspace")
CreateWorkspace(x, y, OutputWorkspace="ws_to_normalise")
CreateWorkspace(x, y_norm, OutputWorkspace="ws_correctly_normalised")
AddSampleLog(Workspace='ws_to_normalise',
LogName="analysis_asymmetry_norm",
LogText="1")
innerFunction = FunctionFactory.createInitialized(
'name=GausOsc,A=0.20,Sigma=0.2,Frequency=1.0,Phi=0')
tf_function = ConvertFitFunctionForMuonTFAsymmetry(
InputFunction=innerFunction, WorkspaceList=['ws_to_normalise'])
CalculateMuonAsymmetry(
MaxIterations=100,
EnableDoublePulse=True,
PulseOffset=delta,
UnNormalizedWorkspaceList='unnormalised_workspace',
ReNormalizedWorkspaceList='ws_to_normalise',
OutputFitWorkspace='DoublePulseFit',
StartX=0,
InputFunction=str(tf_function),
Minimizer='Levenberg-Marquardt')
double_parameter_workspace = AnalysisDataService.retrieve(
'DoublePulseFit_Parameters')
values_column = double_parameter_workspace.column(1)
# Check that the correct normalisation is found.
self.assertAlmostEqual(values_column[0], N0, places=3)
# Check that normalised data is correct
result, message = CompareWorkspaces('ws_to_normalise',
'ws_correctly_normalised',
Tolerance=1e-3)
self.assertTrue(result)
AnalysisDataService.clear()
def tearDown(self):
AnalysisDataService.clear()
