def setUpClass(cls):
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)
y = y1 / 2 + y2 / 2
ws = CreateWorkspace(x, y)
convolution = FunctionFactory.createCompositeFunction('Convolution')
innerFunction = FunctionFactory.createInitialized('name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0')
deltaFunctions = FunctionFactory.createInitialized(
'(name=DeltaFunction,Height=0.5,Centre={},ties=(Height=0.5,Centre={});name=DeltaFunction,Height=0.5,'
'Centre={},ties=(Height=0.5,Centre={}))'.format(
-delta / 2, -delta / 2, delta / 2, delta / 2))
convolution.setAttributeValue('FixResolution', False)
convolution.add(innerFunction)
convolution.add(deltaFunctions)
MultiDomainSingleFunction = FunctionFactory.createInitializedMultiDomainFunction(
'name=GausOsc,A=0.2,Sigma=0.2,Frequency=1,Phi=0', 2)
MultiDomainConvolutionFunction = FunctionFactory.createInitializedMultiDomainFunction(str(convolution), 2)
DoublePulseFit(Function=MultiDomainSingleFunction, InputWorkspace=ws, InputWorkspace_1=ws, CreateOutput=True,
PulseOffset=delta, StartX=0.0, EndX=15.0, Output='DoublePulseFit', MaxIterations=1)
Fit(Function=MultiDomainConvolutionFunction, InputWorkspace=ws, InputWorkspace_1=ws, CreateOutput=True,
StartX=0.0, EndX=15.0, Output='Fit', MaxIterations=1)
def setUp(self):
context = setup_context()
self.model = GeneralFittingModel(context, context.fitting_context)
self.dataset_names = ["Name1", "Name2"]
self.fit_function = FunctionFactory.createFunction("FlatBackground")
self.single_fit_functions = [self.fit_function.clone(), self.fit_function.clone()]
self.simultaneous_fit_function = FunctionFactory.createInitializedMultiDomainFunction(str(self.fit_function),
len(self.dataset_names))
def setUp(self):
context = setup_context()
self.model = TFAsymmetryFittingModel(context, context.fitting_context)
self.dataset_names = [
"EMU20884; Group; fwd; Asymmetry",
"EMU20884; Group; top; Asymmetry"
]
self.tf_non_compliant_dataset_names = [
"EMU20884; Group; fwd; Asymmetry",
"EMU20884; Pair Asym; long; Asymmetry"
]
self.fit_function = FunctionFactory.createFunction("FlatBackground")
self.single_fit_functions = [
self.fit_function.clone(),
self.fit_function.clone()
]
self.simultaneous_fit_function = FunctionFactory.createInitializedMultiDomainFunction(
str(self.fit_function), len(self.dataset_names))
self.tf_single_function = create_tf_asymmetry_function(
self.fit_function.clone())
self.tf_single_fit_functions = [
self.tf_single_function.clone(),
self.tf_single_function.clone()
]
self.tf_simultaneous_fit_function = FunctionFactory.createInitializedMultiDomainFunction(
str(self.tf_single_function), len(self.dataset_names))
CreateSampleWorkspace(Function="One Peak",
XMin=0.0,
XMax=15.0,
BinWidth=0.1,
OutputWorkspace=self.dataset_names[0])
CreateSampleWorkspace(Function="One Peak",
XMin=0.0,
XMax=15.0,
BinWidth=0.1,
OutputWorkspace=self.dataset_names[1])
def test_that_update_attribute_value_will_update_the_value_of_a_parameter_in_simultaneous_fit_mode(self):
self.model.dataset_names = self.dataset_names
self.fit_function = FunctionFactory.createFunction("Chebyshev")
self.model.simultaneous_fit_function = FunctionFactory.createInitializedMultiDomainFunction(
str(self.fit_function), len(self.dataset_names))
self.model.simultaneous_fitting_mode = True
self.model.current_dataset_index = 1
self.model.update_attribute_value("StartX", 0.0)
self.model.update_attribute_value("EndX", 15.0)
self.assertEqual(str(self.model.simultaneous_fit_function), "composite=MultiDomainFunction,NumDeriv=true;"
"name=Chebyshev,EndX=1,StartX=-1,n=0,A0=0,$domains=i;"
"name=Chebyshev,EndX=15,StartX=0,n=0,A0=0,$domains=i")
