def test_guess_from_peak2d():
"""Regression test for guess_from_peak2d function (see GH #627)."""
x = np.linspace(-5, 5)
y = np.linspace(-5, 5)
amplitude = 0.8
centerx = 1.7
sigmax = 0.3
centery = 1.3
sigmay = 0.2
z = lineshapes.gaussian2d(x,
y,
amplitude=amplitude,
centerx=centerx,
sigmax=sigmax,
centery=centery,
sigmay=sigmay)
model = models.Gaussian2dModel()
guess_increasing_x = model.guess(z, x=x, y=y)
guess_decreasing_x = model.guess(z[::-1], x=x[::-1], y=y[::-1])
assert guess_increasing_x == guess_decreasing_x
for param, value in zip(['centerx', 'centery'], [centerx, centery]):
assert np.abs((guess_increasing_x[param].value - value) / value) < 0.5
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()
mod = models.Gaussian2dModel()
params = mod.make_params(amplitude=1.0,
centerx=0.0,
sigmax=0.9,
centery=0.0,
sigmay=0.9)
params.update_constraints()