def test_fix(self):
g = FunctionWrapper( "Gaussian", Height=8.5, Sigma=1.2, PeakCentre=15)
g.fix("Sigma")
g_str = str(g)
self.assertEqual(g_str.count("ties="),1)
self.assertEqual(g_str.count("ties=(Sigma=1.2)"),1)
g0 = FunctionWrapper( "Gaussian", Height=7.5, Sigma=1.2, PeakCentre=10)
g1 = FunctionWrapper( "Gaussian", Height=8.5, Sigma=1.2, PeakCentre=11)
c = CompositeFunctionWrapper(g0, g1)
c.fix("f1.Sigma")
c_str = str(c)
self.assertEqual(c_str.count("ties="),1)
self.assertEqual(c_str.count("ties=(Sigma=1.2"),1)
# remove non-existent tie and test it has no effect
c.untie("f1.Height")
cu_str = str(c)
self.assertEqual(c_str, cu_str)
# remove actual tie
c.untie("f1.Sigma")
cz_str = str(c)
self.assertEqual(cz_str.count("ties="),0)
def test_fix(self):
g = FunctionWrapper( "Gaussian", Height=8.5, Sigma=1.2, PeakCentre=15)
g.fix("Sigma")
g_str = str(g)
self.assertEqual(g_str.count("ties="),1)
self.assertEqual(g_str.count("ties=(Sigma=1.2)"),1)
g0 = FunctionWrapper( "Gaussian", Height=7.5, Sigma=1.2, PeakCentre=10)
g1 = FunctionWrapper( "Gaussian", Height=8.5, Sigma=1.2, PeakCentre=11)
c = CompositeFunctionWrapper(g0, g1)
c.fix("f1.Sigma")
c_str = str(c)
self.assertEqual(c_str.count("ties="),1)
self.assertEqual(c_str.count("ties=(Sigma=1.2"),1)
# remove non-existent tie and test it has no effect
c.untie("f1.Height")
cu_str = str(c)
self.assertEqual(c_str, cu_str)
# remove actual tie
c.untie("f1.Sigma")
cz_str = str(c)
self.assertEqual(cz_str.count("ties="),0)
def do_a_fit(x, function, guess, target, fixes=None, atol=0.01):
r"""Carry out a fit and compare to target parameters
Parameters
----------
x : sequence of floats
Domain values for evaluating the function .
function : str
Registered function name.
guess : dict
Parameter names with their initial values.
target : dict
Parameter names with the values to be obtained after the fit.
fixes : list
List of fitting parameters to fix during the fit
atol : float
Absolute tolerance parameter when evaluating expected_output against
output values.
Returns
-------
list
[0] Evaluation of the comparison between evaluated and expected values.
[1] output of the call to Fit algorithm
"""
target_model = FunctionWrapper(function, **target)
y = target_model(x)
e = np.ones(len(x))
w = CreateWorkspace(x, y, e, Nspec=1)
model = FunctionWrapper(function, **guess)
if fixes is not None:
[model.fix(p) for p in fixes]
fit = Fit(model, w, NIterations=2000)
otarget = OrderedDict(target)
return np.allclose([fit.Function[p] for p in otarget.keys()],
list(otarget.values()), atol), fit
