def test_ellipticity_in_profiles(self):
lightProfile = ['HERNQUIST_ELLIPSE', 'PJAFFE_ELLIPSE']
kwargs_profile = [{
'Rs': 0.16350224766074103,
'q': 0.4105628122365978,
'center_x': -0.019983826426838536,
'center_y': 0.90000011282957304,
'phi_G': 0.14944144075912402,
'sigma0': 1.3168943578511678
}, {
'Rs': 0.29187068596715743,
'q': 0.70799587973181288,
'center_x': 0.020568531548241405,
'center_y': 0.036038490364800925,
'Ra': 0.020000382843298824,
'phi_G': -0.37221683730659516,
'sigma0': 85.948773973262391
}]
kwargs_options = {
'lens_model_list': ['SPEMD'],
'lens_model_internal_bool': [True],
'lens_light_model_internal_bool': [True, True],
'lens_light_model_list': lightProfile
}
lensAnalysis = LensAnalysis(kwargs_options, {})
r_eff = lensAnalysis.half_light_radius(kwargs_profile)
kwargs_profile[0]['q'] = 1
kwargs_profile[1]['q'] = 1
r_eff_spherical = lensAnalysis.half_light_radius(kwargs_profile)
npt.assert_almost_equal(r_eff / r_eff_spherical, 1, decimal=2)
def test_sersic_vs_hernquist_kinematics(self):
"""
attention: this test only works for Sersic indices > \approx 2!
Lower n_sersic will result in different predictions with the Hernquist assumptions
replacing the correct Light model!
:return:
"""
# anisotropy profile
anisotropy_type = 'OsipkovMerritt'
r_ani = 2.
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
# aperture as slit
aperture_type = 'slit'
length = 3.8
width = 0.9
kwargs_aperture = {
'length': length,
'width': width,
'center_ra': 0,
'center_dec': 0,
'angle': 0
}
psf_fwhm = 0.7 # Gaussian FWHM psf
kwargs_cosmo = {'D_d': 1000, 'D_s': 1500, 'D_ds': 800}
# light profile
light_profile_list = ['SERSIC']
r_sersic = .3
n_sersic = 2.8
kwargs_light = [{
'I0_sersic': 1.,
'R_sersic': r_sersic,
'n_sersic': n_sersic
}] # effective half light radius (2d projected) in arcsec
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_profile = [{
'theta_E': theta_E,
'gamma': gamma
}] # Einstein radius (arcsec) and power-law slope
# Hernquist fit to Sersic profile
lens_analysis = LensAnalysis(
{
'lens_light_model_list': ['SERSIC'],
'lens_model_list': ['NONE']
}, {})
r_eff = lens_analysis.half_light_radius(kwargs_light)
print(r_eff)
light_profile_list_hernquist = ['HERNQUIST']
kwargs_light_hernquist = [{'Rs': r_eff * 0.551, 'sigma0': 1.}]
# mge of light profile
lightModel = LightModel(light_profile_list)
r_array = np.logspace(-3, 2, 100) * r_eff * 2
print(r_sersic / r_eff, 'r_sersic/r_eff')
flux_r = lightModel.surface_brightness(r_array, 0, kwargs_light)
amps, sigmas, norm = mge.mge_1d(r_array, flux_r, N=20)
light_profile_list_mge = ['MULTI_GAUSSIAN']
kwargs_light_mge = [{'amp': amps, 'sigma': sigmas}]
print(amps, sigmas, 'amp', 'sigma')
galkin = Galkin(mass_profile_list,
light_profile_list_hernquist,
aperture_type=aperture_type,
anisotropy_model=anisotropy_type,
fwhm=psf_fwhm,
kwargs_cosmo=kwargs_cosmo)
sigma_v = galkin.vel_disp(kwargs_profile, kwargs_light_hernquist,
kwargs_anisotropy, kwargs_aperture)
galkin = Galkin(mass_profile_list,
light_profile_list_mge,
aperture_type=aperture_type,
anisotropy_model=anisotropy_type,
fwhm=psf_fwhm,
kwargs_cosmo=kwargs_cosmo)
sigma_v2 = galkin.vel_disp(kwargs_profile, kwargs_light_mge,
kwargs_anisotropy, kwargs_aperture)
print sigma_v, sigma_v2, 'sigma_v Galkin, sigma_v MGEn'
print(sigma_v / sigma_v2)**2
npt.assert_almost_equal((sigma_v - sigma_v2) / sigma_v2, 0, decimal=1)