def test_height_and_fwhm_expression_evalution_in_builtin_models():
"""Assert models do not throw an ZeroDivisionError."""
mod = models.GaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.LorentzianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.SplitLorentzianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, sigma_r=1.0)
params.update_constraints()
mod = models.VoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=1.0)
params.update_constraints()
mod = models.PseudoVoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, fraction=0.5)
params.update_constraints()
mod = models.MoffatModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, beta=0.0)
params.update_constraints()
mod = models.Pearson7Model()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, expon=1.0)
params.update_constraints()
mod = models.StudentsTModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.BreitWignerModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, q=0.0)
params.update_constraints()
mod = models.LognormalModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.DampedOscillatorModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9)
params.update_constraints()
mod = models.DampedHarmonicOscillatorModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.ExponentialGaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.SkewedGaussianModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.SkewedVoigtModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0,
skew=0.0)
params.update_constraints()
mod = models.DoniachModel()
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, gamma=0.0)
params.update_constraints()
mod = models.StepModel()
for f in ('linear', 'arctan', 'erf', 'logistic'):
params = mod.make_params(amplitude=1.0, center=0.0, sigma=0.9, form=f)
params.update_constraints()
mod = models.RectangleModel()
for f in ('linear', 'arctan', 'erf', 'logistic'):
params = mod.make_params(amplitude=1.0, center1=0.0, sigma1=0.0,
center2=0.0, sigma2=0.0, form=f)
params.update_constraints()
def test_guess_modelparams():
"""Tests for the 'guess' function of built-in models."""
x = np.linspace(-10, 10, 501)
mod = models.ConstantModel()
y = 6.0 + x*0.005
pars = mod.guess(y)
assert_allclose(pars['c'].value, 6.0, rtol=0.01)
mod = models.ComplexConstantModel(prefix='f_')
y = 6.0 + x*0.005 + (4.0 - 0.02*x)*1j
pars = mod.guess(y)
assert_allclose(pars['f_re'].value, 6.0, rtol=0.01)
assert_allclose(pars['f_im'].value, 4.0, rtol=0.01)
mod = models.QuadraticModel(prefix='g_')
y = -0.2 + 3.0*x + 0.005*x**2
pars = mod.guess(y, x=x)
assert_allclose(pars['g_a'].value, 0.005, rtol=0.01)
assert_allclose(pars['g_b'].value, 3.0, rtol=0.01)
assert_allclose(pars['g_c'].value, -0.2, rtol=0.01)
mod = models.PolynomialModel(4, prefix='g_')
y = -0.2 + 3.0*x + 0.005*x**2 - 3.3e-6*x**3 + 1.e-9*x**4
pars = mod.guess(y, x=x)
assert_allclose(pars['g_c0'].value, -0.2, rtol=0.01)
assert_allclose(pars['g_c1'].value, 3.0, rtol=0.01)
assert_allclose(pars['g_c2'].value, 0.005, rtol=0.1)
assert_allclose(pars['g_c3'].value, -3.3e-6, rtol=0.1)
assert_allclose(pars['g_c4'].value, 1.e-9, rtol=0.1)
mod = models.GaussianModel(prefix='g_')
y = lineshapes.gaussian(x, amplitude=2.2, center=0.25, sigma=1.3)
y += np.random.normal(size=len(x), scale=0.004)
pars = mod.guess(y, x=x)
assert_allclose(pars['g_amplitude'].value, 3, rtol=2)
assert_allclose(pars['g_center'].value, 0.25, rtol=1)
assert_allclose(pars['g_sigma'].value, 1.3, rtol=1)
mod = models.LorentzianModel(prefix='l_')
pars = mod.guess(y, x=x)
assert_allclose(pars['l_amplitude'].value, 3, rtol=2)
assert_allclose(pars['l_center'].value, 0.25, rtol=1)
assert_allclose(pars['l_sigma'].value, 1.3, rtol=1)
mod = models.SplitLorentzianModel(prefix='s_')
pars = mod.guess(y, x=x)
assert_allclose(pars['s_amplitude'].value, 3, rtol=2)
assert_allclose(pars['s_center'].value, 0.25, rtol=1)
assert_allclose(pars['s_sigma'].value, 1.3, rtol=1)
assert_allclose(pars['s_sigma_r'].value, 1.3, rtol=1)
mod = models.VoigtModel(prefix='l_')
pars = mod.guess(y, x=x)
assert_allclose(pars['l_amplitude'].value, 3, rtol=2)
assert_allclose(pars['l_center'].value, 0.25, rtol=1)
assert_allclose(pars['l_sigma'].value, 1.3, rtol=1)
mod = models.SkewedVoigtModel(prefix='l_')
pars = mod.guess(y, x=x)
assert_allclose(pars['l_amplitude'].value, 3, rtol=2)
assert_allclose(pars['l_center'].value, 0.25, rtol=1)
assert_allclose(pars['l_sigma'].value, 1.3, rtol=1)