def test_peak_like(peakdata):
# mu = 0
# variance = 1.0
# sigma = np.sqrt(variance)
# x = np.linspace(mu - 20*sigma, mu + 20*sigma, 100.0)
# y = norm.pdf(x, mu, 1)
x = peakdata[0]
y = peakdata[1]
check_height_fwhm(x, y, lineshapes.voigt, models.VoigtModel())
check_height_fwhm(x, y, lineshapes.pvoigt, models.PseudoVoigtModel())
check_height_fwhm(x, y, lineshapes.pearson7, models.Pearson7Model())
check_height_fwhm(x, y, lineshapes.moffat, models.MoffatModel())
check_height_fwhm(x, y, lineshapes.students_t, models.StudentsTModel())
check_height_fwhm(x, y, lineshapes.breit_wigner, models.BreitWignerModel())
check_height_fwhm(x, y, lineshapes.damped_oscillator,
models.DampedOscillatorModel())
check_height_fwhm(x, y, lineshapes.dho,
models.DampedHarmonicOscillatorModel())
check_height_fwhm(x, y, lineshapes.expgaussian,
models.ExponentialGaussianModel())
check_height_fwhm(x, y, lineshapes.skewed_gaussian,
models.SkewedGaussianModel())
check_height_fwhm(x, y, lineshapes.donaich, models.DonaichModel())
x = x - 9 # Lognormal will only fit peaks with centers < 1
check_height_fwhm(x, y, lineshapes.lognormal, models.LognormalModel())
def test_DonaichModel_emits_futurewarning():
"""Assert that using the wrong spelling emits a FutureWarning."""
msg = ('Please correct the name of your built-in model: DonaichModel --> '
'DoniachModel. The incorrect spelling will be removed in a later '
'release.')
with pytest.warns(FutureWarning, match=msg):
models.DonaichModel()
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.DonaichModel()
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()
