def test_mixed_vector_length():
#================================================================
"Check the definition of scalar nonlinear parameters and vector linear parameters"
model = Model(gauss2_scaled)
model.addlinear('gaussian', vec=100)
assert model.Nparam == 101
def test_mixed_vector_names():
#================================================================
"Check the definition of scalar nonlinear parameters and vector linear parameters"
model = Model(gauss2_scaled)
model.addlinear('gaussian', vec=100)
assert 'gaussian' in model.__dict__ and 'scale' in model.__dict__
def test_freeze_vec_outofbounds():
#================================================================
"Check that a parameter cannot be frozen outside of the bounds"
model = Model(gauss2_identity)
model.addlinear('gaussian', vec=100, lb=0)
with pytest.raises(ValueError):
model.gaussian.freeze(np.full(100, -10))
def test_addnonlinear_names():
#================================================================
"Check that linear parameters can be properly added"
model = Model(gauss)
model.addnonlinear('trivial1', lb=0)
model.addnonlinear('trivial2', lb=0)
assert hasattr(model, 'trivial1') and hasattr(model, 'trivial2')
def test_addnonlinear_length():
#================================================================
"Check that the model is contructed correctly with the appropiate number of parameters"
model = Model(gauss)
model.addnonlinear('trivial1', lb=0)
model.addnonlinear('trivial2', lb=0)
assert model.Nparam == 4
def test_preserve_original():
"Check that the original model is not changed by the function"
model = Model(gauss)
model.mean.par0 = 3
model.width.par0 = 0.2
_ = fit(model, mock_data)
assert model._parameter_list() == ['mean', 'width']
def test_addlinear_vector_set():
#================================================================
"Check that attributes of the vector linear parameters are editable"
model = Model(gauss2_identity)
model.addlinear('gaussian', vec=100)
model.gaussian.set(lb=np.zeros(100))
assert np.allclose(getattr(model.gaussian, 'lb'), np.zeros(100))
def test_addlinear_set():
#================================================================
"Check that attributes of the linear parameters are editable"
model = Model(gauss2_design)
model.addlinear('amp1')
model.amp1.set(lb=0, ub=10)
assert getattr(model.amp1, 'lb') == 0 and getattr(model.amp1, 'ub') == 10
def test_addlinear_names():
#================================================================
"Check that linear parameters can be properly added"
model = Model(gauss2_design)
model.addlinear('amp1', lb=0)
model.addlinear('amp2', lb=0)
assert 'amp1' in model.__dict__ and 'amp2' in model.__dict__
def test_addlinear_length():
#================================================================
"Check that the model is contructed correctly with the appropiate number of parameters"
model = Model(gauss2_design)
model.addlinear('amp1', lb=0)
model.addlinear('amp2', lb=0)
assert model.Nparam == 6
def test_mixed_vector_call_positional():
#================================================================
"Check that calling the model with scalar and vector parameters returns the correct response"
model = Model(gauss2_scaled)
model.addlinear('gaussian', vec=len(x))
reference = 5 * gauss2(3, 4, 0.2, 0.3, 0.5, 0.4)
response = model(5, gauss2(3, 4, 0.2, 0.3, 0.5, 0.4))
assert np.allclose(response, reference)
def test_addnonlinear_set():
#================================================================
"Check that attributes of the linear parameters are editable"
model = Model(gauss)
model.addnonlinear('trivial1')
model.trivial1.set(lb=0, ub=10)
assert getattr(model.trivial1, 'lb') == 0 and getattr(
model.trivial1, 'ub') == 10
def test_addlinear_call_mixed():
#================================================================
"Check that calling the model with parameters returns the correct response"
model = Model(gauss2_design)
model.addlinear('amp1')
model.addlinear('amp2')
response = model(3, 4, 0.2, width2=0.3, amp1=0.5, amp2=0.4)
reference = gauss2(3, 4, 0.2, width2=0.3, amp1=0.5, amp2=0.4)
assert np.allclose(response, reference)
def test_addnonlinear_call_positional():
#================================================================
"Check that calling the model with parameters returns the correct response"
model = Model(gauss)
model.addnonlinear('trivial1')
model.addnonlinear('trivial2')
response = model(3, 0.2, 1, 1)
reference = gauss(3, 0.2)
assert np.allclose(response, reference)
def test_addnonlinear_call_keywords():
#================================================================
"Check that calling the model with parameters returns the correct response"
model = Model(gauss)
model.addnonlinear('trivial1')
model.addnonlinear('trivial2')
response = model(mean=3, width=0.2, trivial1=1, trivial2=1)
reference = gauss(mean=3, width=0.2)
assert np.allclose(response, reference)
def test_addlinear_vector_call_keywords():
#================================================================
"Check that calling the model with scalar and vector parameters returns the correct response"
model = Model(gauss2_identity)
model.addlinear('gaussian', vec=len(x))
reference = gauss2(mean1=3,
mean2=4,
width1=0.2,
width2=0.3,
amp1=0.5,
amp2=0.4)
response = model(gaussian=reference)
assert np.allclose(response, reference)
def freedist(r):
def _nonparametric():
return np.eye(len(r))
# Create model
dd_nonparametric = Model(_nonparametric, constants='r')
dd_nonparametric.description = 'Non-parametric distribution model'
# Parameters
dd_nonparametric.addlinear(
'P',
vec=len(r),
lb=0,
par0=0,
description='Non-parametric distance distribution')
return dd_nonparametric
def test_freeze_outofbounds():
#================================================================
"Check that a parameter cannot be frozen outside of the bounds"
model = Model(gauss)
model.mean.set(lb=0, ub=10)
with pytest.raises(ValueError):
model.mean.freeze(-10)
def test_parameters_set():
#================================================================
"Check that attributes of the parameters are editable"
model = Model(gauss)
model.mean.set(lb=0, ub=10)
assert getattr(model.mean, 'lb') == 0 and getattr(model.mean, 'ub') == 10
def test_freeze():
#================================================================
"Check that a model parameter can be frozen to a fixed value"
model = Model(gauss)
model.mean.freeze(3)
assert model.mean.value == 3 and model.mean.frozen == True
def test_call_positional():
#================================================================
"Check that calling the model with parameter returns the correct response"
model = Model(gauss)
response = model(3, 0.5)
reference = gauss(3, 0.5)
assert np.allclose(response, reference)
def test_call_keywords():
#================================================================
"Check that calling the model with parameter returns the correct response"
model = Model(gauss)
response = model(mean=3, width=0.5)
reference = gauss(mean=3, width=0.5)
assert np.allclose(response, reference)
def test_unfreeze():
#================================================================
"Check that a model parameter can be frozen and then reversed"
model = Model(gauss)
model.mean.freeze(3)
model.mean.unfreeze()
assert model.mean.frozen == False and model.mean.value == None
def test_model_constant_same_values_positional():
#================================================================
"Check that a model with axis can be defined and called with same values as parameters"
model = Model(gauss_axis, constants='axis')
reference = gauss_axis(3, 3, 0.5)
response = model(3, 3, 0.5)
assert np.allclose(reference, response)
def test_model_constant_same_values_keywords():
#================================================================
"Check that a model with axis can be defined and called with same values as parameters"
model = Model(gauss_axis, constants='axis')
reference = gauss_axis(axis=3, mean=3, width=0.5)
response = model(axis=3, mean=3, width=0.5)
assert np.allclose(reference, response)
def test_real_model_complex_data():
# ======================================================================
"Check the fit of a real-valued model to complex-valued data"
mymodel = Model(gauss)
mymodel.mean.set(par0=4, lb=1, ub=6)
mymodel.width.set(par0=0.2, lb=0.05, ub=5)
fitResult = fit(mymodel, y.real + 1j * np.zeros_like(y.imag))
assert np.allclose(fitResult.model.real, y.real) and np.allclose(
fitResult.model.imag, np.zeros_like(y.real))
def test_model_with_constant_mixed():
#================================================================
"Check that a model with axis can be defined and called via keywords"
model = Model(gauss_axis, constants='axis')
x = np.linspace(0, 10, 300)
reference = gauss_axis(x, 3, 0.5)
response = model(x, mean=3, width=0.5)
assert np.allclose(reference, response)
def test_model_with_constant_positional():
#================================================================
"Check that a model with axis can be defined and called"
model = Model(gauss_axis, constants='axis')
x = np.linspace(0, 10, 300)
reference = gauss_axis(x, 3, 0.5)
response = model(x, 3, 0.5)
assert np.allclose(reference, response)
def test_model_with_multiple_constants():
#================================================================
"Check that a model with axis can be defined and called"
model = Model(gauss_multiaxis, constants=['axis1', 'axis2'])
x1 = np.linspace(0, 5, 300)
x2 = np.linspace(5, 10, 300)
reference = gauss_multiaxis(x1, x2, 3, 0.5)
response = model(x1, x2, 3, 0.5)
assert np.allclose(reference, response)
def _getmodel(type):
if type == 'parametric':
model = Model(gauss2)
model.mean1.set(lb=0, ub=10, par0=2)
model.mean2.set(lb=0, ub=10, par0=4)
model.width1.set(lb=0.1, ub=5, par0=0.2)
model.width2.set(lb=0.1, ub=5, par0=0.2)
model.amp1.set(lb=0, ub=5, par0=1)
model.amp2.set(lb=0, ub=5, par0=1)
elif type == 'semiparametric':
model = Model(gauss2_design)
model.mean1.set(lb=0, ub=10, par0=2)
model.mean2.set(lb=0, ub=10, par0=4)
model.width1.set(lb=0.1, ub=5, par0=0.2)
model.width2.set(lb=0.1, ub=5, par0=0.2)
model.addlinear('amp1', lb=0, ub=5)
model.addlinear('amp2', lb=0, ub=5)
elif type == 'nonparametric':
model = Model(gauss2_design(3, 4, 0.5, 0.2))
model.addlinear('amp1', lb=0)
model.addlinear('amp2', lb=0)
return model
