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':