def test_vec_Nparam():
"Check that the combined model a vector-form parameter has the right number of parameters"
model1 = dl.dd_gauss2
model2 = model_vec
model = lincombine(model1, model2)
assert model.Nparam == model1.Nparam + model2.Nparam
def test_threemodels_addweights():
"Check that that weights can be introduced properly"
model1 = dl.dd_gauss
model2 = dl.dd_rice
model3 = dl.dd_wormchain
model = lincombine(model1, model2, model3, addweights=True)
x = np.linspace(0, 10, 400)
ref = model1(x, 3, 0.2) + model2(x, 4, 0.5) + model3(x, 3.7, 10)
response = model(r_1=x,
r_2=x,
r_3=x,
mean_1=3,
width_1=0.2,
location_2=4,
spread_2=0.5,
contour_3=3.7,
persistence_3=10,
scale_1=1,
scale_2=1,
scale_3=1,
weight_1=1,
weight_2=1,
weight_3=1)
assert np.allclose(response, ref)
def test_twomodels_Nparam_lin():
"Check that the combined model has the right number of parameters"
model1 = dl.dd_gauss2
model2 = dl.dd_gauss3
model = lincombine(model1, model2)
assert model.Nlin == model1.Nlin + model2.Nlin
def test_twomodels_Nparam_list():
"Check that the combined model has the right number of parameters"
model1 = dl.dd_gauss3
model2 = dl.dd_gauss2
model = lincombine(model1, model2)
assert model.Nparam == len(model._parameter_list())
def test_twomodels_addweights_values():
"Check that that weights values work properly"
model1 = dl.dd_gauss
model2 = dl.dd_gauss
model = lincombine(model1, model2, addweights=True)
x = np.linspace(0, 10, 400)
ref1 = model1(x, 3, 0.2)
ref2 = model2(x, 4, 0.5)
response1 = 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=0)
response2 = 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=0,
weight_2=1)
assert np.allclose(response1, ref1) and np.allclose(response2, ref2)
def test_vec_Nparam_nonlin():
"Check that the combined model with a vector-form parameter has the right number of parameters"
model1 = dl.dd_gauss
model2 = model_vec
model = lincombine(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 = lincombine(model1, model2)
assert isinstance(model, Model)
def test_threemodels_Nparam():
"Check that the combined model has the right number of parameters"
model1 = dl.dd_gauss3
model2 = dl.dd_gauss2
model3 = dl.dd_gauss2
model = lincombine(model1, model2, model3)
assert model.Nparam == model1.Nparam + model2.Nparam + model3.Nparam
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 = lincombine(model1, model2)
assert 'scale_2' in 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
_ = lincombine(model1, model2)
assert model1._parameter_list() == [
'mean', 'width'
] and model2._parameter_list() == ['conc', 'lam']
def test_twomodels_param_names():
"Check that the combined model has the adjusted parameter names"
model1 = dl.dd_gauss
model2 = dl.dd_gauss
model = lincombine(model1, model2)
assert all([
str in model._parameter_list()
for str in ['mean_1', 'mean_2', 'width_1', 'width_2']
])
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_gauss
model3 = dl.dd_gauss
model = lincombine(model1, model2, model3)
assert [
scale_par in model._parameter_list()
for scale_par in ['scale_1', 'scale_2', 'scale_3']
]
def test_fit_model():
"Check that that lincombine works correctly for two models"
model1 = dl.dd_gauss
model2 = dl.dd_rice
model = lincombine(model1, model2)
x = np.linspace(0, 10, 400)
truth = model1(x, 3, 0.2) + model2(x, 4, 0.5)
model.mean_1.par0 = 3
model.location_2.par0 = 4
result = fit(model, truth, x, x)
assert np.allclose(result.model, truth)
def test_twomodels_call():
"Check that that lincombine works correctly for two models"
model1 = dl.dd_gauss
model2 = dl.dd_rice
model = lincombine(model1, model2)
x = np.linspace(0, 10, 400)
ref1 = model1(x, 3, 0.2)
ref2 = model2(x, 4, 0.5)
ref = ref1 + ref2
response = model(x, x, 3, 0.2, 4, 0.5, 1, 1)
assert np.allclose(response, ref)
def test_vec_two_models():
"Check that that lincombine works correctly for two models"
model1 = dl.dd_gauss
model2 = model_vec
model = lincombine(model1, model2)
x = np.linspace(0, 10, 100)
ref1 = model1(x, 3, 0.2)
ref2 = model2(r=x, Pvec=model1(x, 4, 0.3))
ref = ref1 + ref2
response = model(x, x, 3, 0.2, 1, model1(x, 4, 0.3))
assert np.allclose(response, ref)
def test_vec_addweights():
"Check that that weights can be introduced properly"
model1 = model_vec
model2 = model_vec
model = lincombine(model1, model2, addweights=True)
x = np.linspace(0, 10, 100)
ref1 = dl.dd_gauss(x, 3, 0.2)
ref2 = dl.dd_gauss(x, 4, 0.2)
ref = ref1 + ref2
response = model(r_1=x,
r_2=x,
Pvec_1=ref1,
Pvec_2=ref2,
weight_1=1,
weight_2=1)
assert np.allclose(response, ref)
def test_twomodels_addweights():
"Check that that weights can be introduced properly"
model1 = dl.dd_gauss
model2 = dl.dd_gauss
model = lincombine(model1, model2, addweights=True)
x = np.linspace(0, 10, 400)
ref = model1(x, 3, 0.2) + 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 np.allclose(response, ref)
def test_threemodels_addweights_values():
"Check that that weights values work properly"
model1 = dl.dd_gauss
model2 = dl.dd_rice
model3 = dl.dd_gauss
model = lincombine(model1, model2, model3, addweights=True)
x = np.linspace(0, 10, 400)
ref1 = model1(x, 3, 0.2)
ref2 = model2(x, 4, 0.5)
ref3 = model3(x, 5, 0.1)
response1 = model(r_1=x,
r_2=x,
r_3=x,
mean_1=3,
width_1=0.2,
location_2=4,
spread_2=0.5,
mean_3=5,
width_3=0.1,
scale_1=1,
scale_2=1,
scale_3=1,
weight_1=1,
weight_2=0,
weight_3=0)
response2 = model(r_1=x,
r_2=x,
r_3=x,
mean_1=3,
width_1=0.2,
location_2=4,
spread_2=0.5,
mean_3=5,
width_3=0.1,
scale_1=1,
scale_2=1,
scale_3=1,
weight_1=0,
weight_2=1,
weight_3=0)
response3 = model(r_1=x,
r_2=x,
r_3=x,
mean_1=3,
width_1=0.2,
location_2=4,
spread_2=0.5,
mean_3=5,
width_3=0.1,
scale_1=1,
scale_2=1,
scale_3=1,
weight_1=0,
weight_2=0,
weight_3=1)
assert all([
np.allclose(response, ref) for response, ref in zip(
[response1, response2, response3], [ref1, ref2, ref3])
])
