def test_vec_Nparam_nonlin():
"Check that the merged model with a vector-form parameter has the right number of parameters"
model1 = dl.dd_gauss
model2 = model_vec
model = merge(model1, model2)
assert model.Nnonlin == model1.Nnonlin + model2.Nnonlin
def test_type():
"Check that the function returns a valid model type"
model1 = dl.dd_gauss
model2 = dl.bg_hom3d
model = merge(model1, model2)
assert isinstance(model, Model)
def test_twomodels_default_linear():
"""Check that the default linear scaling parameter is added if there
are no linear parameters on one of the models"""
model1 = dl.dd_gauss2
model2 = dl.dd_gauss
model = merge(model1, model2)
assert 'scale_2' in model._parameter_list()
def test_threemodels_Nparam_list():
"Check that the merged model has the right number of parameters"
model1 = dl.dd_gauss2
model2 = dl.dd_gauss2
model3 = dl.dd_gauss3
model = merge(model1, model2, model3)
assert model.Nparam == len(model._parameter_list())
def test_preserve_original():
"Check that the original models are not changed by the function"
model1 = dl.dd_gauss
model2 = dl.bg_hom3d
_ = merge(model1, model2)
assert model1._parameter_list() == [
'mean', 'width'
] and model2._parameter_list() == ['conc', 'lam']
def test_threemodels_Nparam_lin():
"Check that the merged model has the right number of parameters"
model1 = dl.dd_gauss2
model2 = dl.dd_gauss2
model3 = dl.dd_gauss3
model = merge(model1, model2, model3)
assert model.Nlin == model1.Nlin + model2.Nlin + model3.Nlin
def test_twomodels_param_names():
"Check that the merged model has the adjusted parameter names"
model1 = dl.dd_gauss
model2 = dl.dd_gauss
model = merge(model1, model2)
assert all([
str in model._parameter_list()
for str in ['mean_1', 'mean_2', 'width_1', 'width_2']
])
def test_fit_model():
"Check that that merge works correctly for two models"
model1 = dl.dd_gauss
model2 = dl.dd_rice
model = merge(model1, model2)
x = np.linspace(0, 10, 400)
truth = [model1(x, 3, 0.2), model2(x, 4, 0.5)]
result = fit(model, truth, x, x, weights=[1, 1])
assert np.allclose(result.model[0], truth[0]) and np.allclose(
result.model[1], truth[1])
def test_vec_twomodels_mixed():
"Check that that merge works correctly for two vector-based models"
model1 = dl.dd_gauss
model2 = model_vec
model = merge(model1, model2)
x = np.linspace(0, 10, 100)
ref1 = model1(x, 3, 0.2)
ref2 = model2(r=x, Pvec=dl.dd_gauss(x, 4, 0.3))
response = model(x, x, 3, 0.2, 1, dl.dd_gauss(x, 4, 0.3))
assert all(
[np.allclose(response[n], ref) for n, ref in enumerate([ref1, ref2])])
def test_threemodels_default_linear():
"""Check that the default linear scaling parameter is added if there
are no linear parameters on one of the models"""
model1 = dl.dd_gauss
model2 = dl.dd_rice
model3 = dl.dd_shell
model = merge(model1, model2, model3)
assert [
scaleparam in model._parameter_list()
for scaleparam in ['scale_1', 'scale_2', 'scale_3']
]
def test_twomodels_call():
"Check that that merge works correctly for two models"
model1 = dl.dd_gauss
model2 = dl.dd_rice
model = merge(model1, model2)
x = np.linspace(0, 10, 400)
ref1 = model1(x, 3, 0.2)
ref2 = model2(x, 4, 0.5)
response = model(x, x, 3, 0.2, 4, 0.5, 1, 1)
assert all(
[np.allclose(response[n], ref) for n, ref in enumerate([ref1, ref2])])
def test_threemodels_addweights():
"Check that that weights can be introduced properly"
model1 = dl.dd_gauss
model2 = dl.dd_gauss
model = merge(model1, model2, addweights=True)
x = np.linspace(0, 10, 400)
ref1 = model1(x, 3, 0.2)
ref2 = model2(x, 4, 0.5)
response = model(r_1=x,
r_2=x,
mean_1=3,
width_1=0.2,
mean_2=4,
width_2=0.5,
scale_1=1,
scale_2=1,
weight_1=1,
weight_2=1)
assert all(
[np.allclose(response[n], ref) for n, ref in enumerate([ref1, ref2])])