x_0=0.5,
y_0=1.5,
R_0=5.,
slope=1.),
astmodels.Voigt1D(x_0=0.55, amplitude_L=10., fwhm_L=0.5, fwhm_G=0.9),
astmodels.BlackBody(scale=10.0, temperature=6000. * u.K),
astmodels.Drude1D(amplitude=10.0, x_0=0.5, fwhm=2.5),
astmodels.Plummer1D(mass=10.0, r_plum=5.0),
astmodels.BrokenPowerLaw1D(amplitude=10,
x_break=0.5,
alpha_1=2.0,
alpha_2=3.5),
astmodels.ExponentialCutoffPowerLaw1D(10, 0.5, 2.0, 7.),
astmodels.LogParabola1D(
amplitude=10,
x_0=0.5,
alpha=2.,
beta=3.,
),
astmodels.PowerLaw1D(amplitude=10., x_0=0.5, alpha=2.0),
astmodels.SmoothlyBrokenPowerLaw1D(amplitude=10.,
x_break=5.0,
alpha_1=2.0,
alpha_2=3.0,
delta=0.5),
custom_and_analytical_inverse(),
custom_inputs_outputs(),
]
if HAS_SCIPY:
test_models.append(
astmodels.Spline1D(
'GaussianAbsorption1D': models.GaussianAbsorption1D(1.0, 1.0, 1.0), 'Lorentz1D': models.Lorentz1D(1.0, 1.0, 1.0), 'MexicanHat1D': models.MexicanHat1D(1.0, 1.0, 1.0), 'Trapezoid1D': models.Trapezoid1D(1.0, 1.0, 1.0, 1.0), 'Moffat1D': models.Moffat1D(1.0, 1.0, 1.0, 1.0), 'ExponentialCutoffPowerLaw1D': models.ExponentialCutoffPowerLaw1D(1.0, 1.0, 1.0, 1.0), 'BrokenPowerLaw1D': models.BrokenPowerLaw1D(1.0, 1.0, 1.0, 1.0), 'LogParabola1D': models.LogParabola1D(1.0, 1.0, 1.0, 1.0), 'PowerLaw1D': models.PowerLaw1D(1.0, 1.0, 1.0), 'Linear1D': models.Linear1D(1.0, 0.0), 'Const1D': models.Const1D(0.0), 'Redshift': models.Redshift(0.0), 'Scale': models.Scale(1.0), 'Shift': models.Shift(0.0), 'Sine1D': models.Sine1D(1.0, 1.0), 'Chebyshev1D':