def test_kinematic_lens_profiles(self):
z_lens = 0.5
z_source = 1.5
kwargs_options = {'lens_model_list': ['SPEP', 'SHEAR']}
kin_api = KinematicsAPI(z_lens,
z_source,
kwargs_options,
kwargs_aperture={},
kwargs_seeing={},
anisotropy_model='OM')
kwargs_lens = [{
'theta_E': 1.4272358196260446,
'e1': 0,
'center_x': -0.044798916793300093,
'center_y': 0.0054408937891703788,
'e2': 0,
'gamma': 1.8
}, {
'e1': -0.050871696555354479,
'e2': -0.0061601733920590464
}]
kwargs_mge = {'n_comp': 20}
mass_profile_list, kwargs_profile = kin_api.kinematic_lens_profiles(
kwargs_lens,
MGE_fit=True,
kwargs_mge=kwargs_mge,
theta_E=1.4,
model_kinematics_bool=[True, False])
assert mass_profile_list[0] == 'MULTI_GAUSSIAN_KAPPA'
mass_profile_list, kwargs_profile = kin_api.kinematic_lens_profiles(
kwargs_lens, MGE_fit=False, model_kinematics_bool=[True, False])
assert mass_profile_list[0] == 'SPEP'
def test_likelihoodconfiguration_gom(self):
anisotropy_model = 'GOM'
kwargs_aperture = {'aperture_type': 'shell', 'r_in': 0, 'r_out': 3 / 2., 'center_ra': 0.0, 'center_dec': 0}
kwargs_seeing = {'psf_type': 'GAUSSIAN', 'fwhm': 1.4}
# numerical settings (not needed if power-law profiles with Hernquist light distribution is computed)
kwargs_numerics_galkin = {'interpol_grid_num': 1000, # numerical interpolation, should converge -> infinity
'log_integration': True,
# log or linear interpolation of surface brightness and mass models
'max_integrate': 100,
'min_integrate': 0.001} # lower/upper bound of numerical integrals
# redshift
z_lens = 0.5
z_source = 1.5
# lens model
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=70, Om0=0.3)
theta_E = 1.
r_eff = 1
gamma = 2.1
# kwargs_model
lens_light_model_list = ['HERNQUIST']
lens_model_list = ['SPP']
kwargs_model = {'lens_model_list': lens_model_list, 'lens_light_model_list': lens_light_model_list}
# settings for kinematics calculation with KinematicsAPI of lenstronomy
kwargs_kin_api_settings = {'multi_observations': False, 'kwargs_numerics_galkin': kwargs_numerics_galkin,
'MGE_light': False, 'kwargs_mge_light': None, 'sampling_number': 1000,
'num_kin_sampling': 1000, 'num_psf_sampling': 100}
kin_api = KinematicsAPI(z_lens, z_source, kwargs_model, kwargs_aperture, kwargs_seeing, anisotropy_model,
cosmo=cosmo, **kwargs_kin_api_settings)
# compute kinematics with fiducial cosmology
kwargs_lens = [{'theta_E': theta_E, 'gamma': gamma, 'center_x': 0, 'center_y': 0}]
kwargs_lens_light = [{'Rs': r_eff * 0.551, 'amp': 1.}]
beta_inf = 0.5
kwargs_anisotropy = {'r_ani': r_eff, 'beta_inf': beta_inf}
sigma_v = kin_api.velocity_dispersion(kwargs_lens, kwargs_lens_light, kwargs_anisotropy, r_eff=r_eff,
theta_E=theta_E, gamma=gamma, kappa_ext=0)
# compute likelihood
kin_constraints = KinConstraints(z_lens=z_lens, z_source=z_source, theta_E=theta_E, theta_E_error=0.01,
gamma=gamma, gamma_error=0.02, r_eff=r_eff, r_eff_error=0.05, sigma_v=[sigma_v],
sigma_v_error_independent=[10], sigma_v_error_covariant=0,
kwargs_aperture=kwargs_aperture, kwargs_seeing=kwargs_seeing,
anisotropy_model=anisotropy_model, **kwargs_kin_api_settings)
kwargs_likelihood = kin_constraints.hierarchy_configuration(num_sample_model=5)
kwargs_likelihood['normalized'] = False
ln_class = LensLikelihood(**kwargs_likelihood)
kwargs_kin = {'a_ani': 1, 'beta_inf': beta_inf}
ln_likelihood = ln_class.lens_log_likelihood(cosmo, kwargs_lens={}, kwargs_kin=kwargs_kin)
npt.assert_almost_equal(ln_likelihood, 0, decimal=1)
def test_kinematic_light_profile(self):
z_lens = 0.5
z_source = 1.5
kwargs_options = {
'lens_light_model_list': ['HERNQUIST_ELLIPSE', 'SERSIC']
}
kwargs_mge = {'n_comp': 20}
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_options,
kwargs_seeing={},
kwargs_aperture={},
anisotropy_model='OM')
r_eff = 0.2
kwargs_lens_light = [{
'amp': 1,
'Rs': r_eff * 0.551,
'e1': 0.,
'e2': 0,
'center_x': 0,
'center_y': 0
}, {
'amp': 1,
'R_sersic': 1,
'n_sersic': 2,
'center_x': -10,
'center_y': -10
}]
light_profile_list, kwargs_light = kinematicAPI.kinematic_light_profile(
kwargs_lens_light,
MGE_fit=True,
r_eff=r_eff,
model_kinematics_bool=[True, False],
kwargs_mge=kwargs_mge)
assert light_profile_list[0] == 'MULTI_GAUSSIAN'
light_profile_list, kwargs_light = kinematicAPI.kinematic_light_profile(
kwargs_lens_light,
MGE_fit=False,
r_eff=r_eff,
model_kinematics_bool=[True, False])
assert light_profile_list[0] == 'HERNQUIST_ELLIPSE'
light_profile_list, kwargs_light = kinematicAPI.kinematic_light_profile(
kwargs_lens_light,
MGE_fit=False,
Hernquist_approx=True,
r_eff=r_eff,
model_kinematics_bool=[True, False])
assert light_profile_list[0] == 'HERNQUIST'
npt.assert_almost_equal(kwargs_light[0]['Rs'] /
kwargs_lens_light[0]['Rs'],
1,
decimal=2)
def test_raise(self):
with self.assertRaises(ValueError):
z_lens = 0.5
z_source = 1.5
kwargs_model = {'lens_light_model_list': ['HERNQUIST']}
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_seeing={},
kwargs_aperture={},
anisotropy_model='OM')
kwargs_light = [{'Rs': 1, 'amp': 1, 'center_x': 0, 'center_y': 0}]
kinematicAPI.kinematic_light_profile(kwargs_light,
MGE_fit=False,
Hernquist_approx=True,
r_eff=None,
model_kinematics_bool=[True])
with self.assertRaises(ValueError):
z_lens = 0.5
z_source = 1.5
kwargs_model = {'lens_light_model_list': ['HERNQUIST']}
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_seeing={},
kwargs_aperture={},
anisotropy_model='OM')
kwargs_light = [{'Rs': 1, 'amp': 1, 'center_x': 0, 'center_y': 0}]
kinematicAPI.kinematic_light_profile(kwargs_light,
MGE_fit=False,
Hernquist_approx=False,
r_eff=None,
analytic_kinematics=True)
with self.assertRaises(ValueError):
z_lens = 0.5
z_source = 1.5
kwargs_model = {
'lens_light_model_list': ['HERNQUIST'],
'lens_model_list': []
}
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_seeing={},
kwargs_aperture={},
anisotropy_model='OM')
kwargs_light = [{'Rs': 1, 'amp': 1, 'center_x': 0, 'center_y': 0}]
kinematicAPI.kinematic_lens_profiles(kwargs_light,
MGE_fit=True,
model_kinematics_bool=[True])
with self.assertRaises(ValueError):
z_lens = 0.5
z_source = 1.5
kwargs_model = {
'lens_light_model_list': ['HERNQUIST'],
'lens_model_list': []
}
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_seeing={},
kwargs_aperture={},
anisotropy_model='OM')
kinematicAPI.kinematic_lens_profiles(kwargs_lens=None,
analytic_kinematics=True)
with self.assertRaises(ValueError):
z_lens = 0.5
z_source = 1.5
kwargs_model = {
'lens_light_model_list': ['HERNQUIST'],
'lens_model_list': []
}
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_seeing={},
kwargs_aperture={},
anisotropy_model='OM')
kwargs_lens_light = [{'Rs': 1, 'center_x': 0, 'center_y': 0}]
kinematicAPI.kinematic_light_profile(kwargs_lens_light,
r_eff=None,
MGE_fit=True,
model_kinematics_bool=None,
Hernquist_approx=False,
kwargs_mge=None)
with self.assertRaises(ValueError):
z_lens = 0.5
z_source = 1.5
kwargs_model = {
'lens_light_model_list': ['HERNQUIST'],
'lens_model_list': ['SIS']
}
kwargs_lens = [{'theta_E': 1, 'center_x': 0, 'center_y': 0}]
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_seeing={},
kwargs_aperture={},
anisotropy_model='OM')
kinematicAPI.kinematic_lens_profiles(kwargs_lens,
MGE_fit=True,
model_kinematics_bool=None,
theta_E=None,
kwargs_mge={})
def test_interpolated_sersic(self):
from lenstronomy.Analysis.light2mass import light2mass_interpol
kwargs_light = [{
'n_sersic': 2,
'R_sersic': 0.5,
'amp': 1,
'center_x': 0.01,
'center_y': 0.01
}]
kwargs_lens = [{
'n_sersic': 2,
'R_sersic': 0.5,
'k_eff': 1,
'center_x': 0.01,
'center_y': 0.01
}]
deltaPix = 0.1
numPix = 100
kwargs_interp = light2mass_interpol(['SERSIC'],
kwargs_lens_light=kwargs_light,
numPix=numPix,
deltaPix=deltaPix,
subgrid_res=5)
kwargs_lens_interp = [kwargs_interp]
from lenstronomy.Analysis.kinematics_api import KinematicsAPI
z_lens = 0.5
z_source = 1.5
r_ani = 0.62
kwargs_anisotropy = {'r_ani': r_ani}
R_slit = 3.8
dR_slit = 1.
aperture_type = 'slit'
kwargs_aperture = {
'center_ra': 0,
'width': dR_slit,
'length': R_slit,
'angle': 0,
'center_dec': 0,
'aperture_type': aperture_type
}
psf_fwhm = 0.7
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
anisotropy_model = 'OM'
r_eff = 0.5
kwargs_model = {
'lens_model_list': ['SERSIC'],
'lens_light_model_list': ['SERSIC']
}
kwargs_mge = {'n_comp': 20}
kinematic_api = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_aperture,
kwargs_seeing=kwargs_psf,
anisotropy_model=anisotropy_model,
MGE_light=True,
MGE_mass=True,
kwargs_mge_mass=kwargs_mge,
kwargs_mge_light=kwargs_mge)
v_sigma = kinematic_api.velocity_dispersion(kwargs_lens,
kwargs_light,
kwargs_anisotropy,
r_eff=r_eff,
theta_E=1)
kwargs_model_interp = {
'lens_model_list': ['INTERPOL'],
'lens_light_model_list': ['SERSIC']
}
kinematic_api_interp = KinematicsAPI(z_lens,
z_source,
kwargs_model_interp,
kwargs_aperture,
kwargs_seeing=kwargs_psf,
anisotropy_model=anisotropy_model,
MGE_light=True,
MGE_mass=True,
kwargs_mge_mass=kwargs_mge,
kwargs_mge_light=kwargs_mge)
v_sigma_interp = kinematic_api_interp.velocity_dispersion(
kwargs_lens_interp,
kwargs_light,
kwargs_anisotropy,
theta_E=1.,
r_eff=r_eff)
npt.assert_almost_equal(v_sigma / v_sigma_interp, 1, 1)
def test_velocity_dispersion_map(self):
np.random.seed(42)
z_lens = 0.5
z_source = 1.5
kwargs_options = {
'lens_model_list': ['SIS'],
'lens_light_model_list': ['HERNQUIST']
}
r_eff = 1.
theta_E = 1
kwargs_lens = [{'theta_E': theta_E, 'center_x': 0, 'center_y': 0}]
kwargs_lens_light = [{
'amp': 1,
'Rs': r_eff * 0.551,
'center_x': 0,
'center_y': 0
}]
kwargs_anisotropy = {'r_ani': 1}
r_bins = np.array([0, 0.5, 1])
aperture_type = 'IFU_shells'
kwargs_aperture = {
'aperture_type': aperture_type,
'center_ra': 0,
'r_bins': r_bins,
'center_dec': 0
}
psf_fwhm = 0.7
kwargs_seeing = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
anisotropy_model = 'OM'
kin_api = KinematicsAPI(z_lens,
z_source,
kwargs_options,
kwargs_aperture=kwargs_aperture,
kwargs_seeing=kwargs_seeing,
anisotropy_model=anisotropy_model)
kwargs_numerics_galkin = {
'interpol_grid_num': 500,
'log_integration': True,
'max_integrate': 10,
'min_integrate': 0.001
}
kin_api.kinematics_modeling_settings(anisotropy_model,
kwargs_numerics_galkin,
analytic_kinematics=True,
Hernquist_approx=False,
MGE_light=False,
MGE_mass=False,
num_kin_sampling=1000,
num_psf_sampling=100)
vel_disp_analytic = kin_api.velocity_dispersion_map(kwargs_lens,
kwargs_lens_light,
kwargs_anisotropy,
r_eff=r_eff,
theta_E=theta_E,
gamma=2)
kin_api.kinematics_modeling_settings(anisotropy_model,
kwargs_numerics_galkin,
analytic_kinematics=False,
Hernquist_approx=False,
MGE_light=False,
MGE_mass=False,
num_kin_sampling=1000,
num_psf_sampling=100)
vel_disp_numerical = kin_api.velocity_dispersion_map(kwargs_lens,
kwargs_lens_light,
kwargs_anisotropy,
r_eff=r_eff,
theta_E=theta_E,
gamma=2)
print(vel_disp_numerical, vel_disp_analytic)
npt.assert_almost_equal(vel_disp_numerical,
vel_disp_analytic,
decimal=-1)
def test_model_dispersion(self):
np.random.seed(42)
z_lens = 0.5
z_source = 1.5
r_eff = 1.
theta_E = 1.
kwargs_model = {
'lens_model_list': ['SIS'],
'lens_light_model_list': ['HERNQUIST']
}
kwargs_lens = [{'theta_E': theta_E, 'center_x': 0, 'center_y': 0}]
kwargs_lens_light = [{
'amp': 1,
'Rs': r_eff * 0.551,
'center_x': 0,
'center_y': 0
}]
kwargs_anisotropy = {'r_ani': 1}
# settings
R_slit = 3.8
dR_slit = 1.
aperture_type = 'slit'
kwargs_aperture = {
'aperture_type': aperture_type,
'center_ra': 0,
'width': dR_slit,
'length': R_slit,
'angle': 0,
'center_dec': 0
}
psf_fwhm = 0.7
kwargs_seeing = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
anisotropy_model = 'OM'
kin_api = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_aperture,
kwargs_seeing,
anisotropy_model=anisotropy_model)
kwargs_numerics_galkin = {
'interpol_grid_num': 2000,
'log_integration': True,
'max_integrate': 1000,
'min_integrate': 0.0001
}
kin_api.kinematics_modeling_settings(anisotropy_model,
kwargs_numerics_galkin,
analytic_kinematics=True,
Hernquist_approx=False,
MGE_light=False,
MGE_mass=False)
vel_disp_analytic = kin_api.velocity_dispersion(kwargs_lens,
kwargs_lens_light,
kwargs_anisotropy,
r_eff=r_eff,
theta_E=theta_E,
gamma=2)
kin_api.kinematics_modeling_settings(anisotropy_model,
kwargs_numerics_galkin,
analytic_kinematics=False,
Hernquist_approx=False,
MGE_light=False,
MGE_mass=False)
vel_disp_numerical = kin_api.velocity_dispersion(
kwargs_lens, kwargs_lens_light, kwargs_anisotropy) #,
# r_eff=r_eff, theta_E=theta_E, gamma=2)
npt.assert_almost_equal(vel_disp_numerical / vel_disp_analytic,
1,
decimal=2)
kin_api.kinematics_modeling_settings(anisotropy_model,
kwargs_numerics_galkin,
analytic_kinematics=False,
Hernquist_approx=False,
MGE_light=False,
MGE_mass=False,
kwargs_mge_light={'n_comp': 10},
kwargs_mge_mass={'n_comp': 5})
assert kin_api._kwargs_mge_mass['n_comp'] == 5
assert kin_api._kwargs_mge_light['n_comp'] == 10
def test_galkin_settings(self):
z_lens = 0.5
z_source = 1.5
kwargs_model = {
'lens_model_list': ['SIS'],
'lens_light_model_list': ['HERNQUIST']
}
kwargs_lens = [{'theta_E': 1, 'center_x': 0, 'center_y': 0}]
kwargs_lens_light = [{'amp': 1, 'Rs': 1, 'center_x': 0, 'center_y': 0}]
r_ani = 0.62
kwargs_anisotropy = {'r_ani': r_ani}
R_slit = 3.8
dR_slit = 1.
aperture_type = 'slit'
kwargs_aperture = {
'aperture_type': aperture_type,
'center_ra': 0,
'width': dR_slit,
'length': R_slit,
'angle': 0,
'center_dec': 0
}
psf_fwhm = 0.7
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
anisotropy_model = 'OM'
kwargs_mge = {'n_comp': 20}
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_seeing=kwargs_psf,
analytic_kinematics=True,
anisotropy_model=anisotropy_model,
kwargs_mge_light=kwargs_mge,
kwargs_mge_mass=kwargs_mge,
sampling_number=1000)
galkin, kwargs_profile, kwargs_light = kinematicAPI.galkin_settings(
kwargs_lens,
kwargs_lens_light,
r_eff=None,
theta_E=None,
gamma=None)
npt.assert_almost_equal(kwargs_profile['gamma'], 2, decimal=2)
kinematicAPI = KinematicsAPI(z_lens,
z_source,
kwargs_model,
kwargs_aperture=[kwargs_aperture],
kwargs_seeing=[kwargs_psf],
analytic_kinematics=True,
anisotropy_model=anisotropy_model,
multi_observations=True,
kwargs_mge_light=kwargs_mge,
kwargs_mge_mass=kwargs_mge,
sampling_number=1000)
galkin, kwargs_profile, kwargs_light = kinematicAPI.galkin_settings(
kwargs_lens,
kwargs_lens_light,
r_eff=None,
theta_E=None,
gamma=None)
npt.assert_almost_equal(kwargs_profile['gamma'], 2, decimal=2)
def test_velocity_dispersion(self):
z_lens = 0.5
z_source = 1.5
kwargs_model = {
'lens_model_list': ['SPEP', 'SHEAR', 'SIS', 'SIS', 'SIS'],
'lens_light_model_list': ['SERSIC_ELLIPSE', 'SERSIC']
}
theta_E = 1.5
gamma = 1.8
kwargs_lens = [{
'theta_E': theta_E,
'e1': 0,
'center_x': -0.044798916793300093,
'center_y': 0.0054408937891703788,
'e2': 0,
'gamma': gamma
}, {
'e1': -0.050871696555354479,
'e2': -0.0061601733920590464
}, {
'center_y': 2.79985456,
'center_x': -2.32019894,
'theta_E': 0.28165274714097904
}, {
'center_y': 3.83985426,
'center_x': -2.32019933,
'theta_E': 0.0038110812674654873
}, {
'center_y': 4.31985428,
'center_x': -1.68019931,
'theta_E': 0.45552039839735037
}]
phi, q = -0.52624727893702705, 0.79703498156919605
e1, e2 = param_util.phi_q2_ellipticity(phi, q)
kwargs_lens_light = [{
'n_sersic': 1.1212528655709217,
'center_x': -0.019674496231393473,
'e1': e1,
'e2': e2,
'amp': 1.1091367792010356,
'center_y': 0.076914975081560991,
'R_sersic': 0.42691611878867058
}, {
'R_sersic': 0.03025682660635394,
'amp': 139.96763298885992,
'n_sersic': 1.90000008624093865,
'center_x': -0.019674496231393473,
'center_y': 0.076914975081560991
}]
r_ani = 0.62
kwargs_anisotropy = {'r_ani': r_ani}
R_slit = 3.8
dR_slit = 1.
aperture_type = 'slit'
kwargs_aperture = {
'aperture_type': aperture_type,
'center_ra': 0,
'width': dR_slit,
'length': R_slit,
'angle': 0,
'center_dec': 0
}
psf_fwhm = 0.7
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
anisotropy_model = 'OM'
kwargs_mge = {'n_comp': 20}
r_eff = 0.211919902322
kinematicAPI = KinematicsAPI(
z_lens,
z_source,
kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_seeing=kwargs_psf,
lens_model_kinematics_bool=[True, False, False, False, False],
anisotropy_model=anisotropy_model,
kwargs_mge_light=kwargs_mge,
kwargs_mge_mass=kwargs_mge,
sampling_number=1000,
MGE_light=True)
v_sigma = kinematicAPI.velocity_dispersion(kwargs_lens,
kwargs_lens_light,
kwargs_anisotropy,
r_eff=r_eff)
kinematicAPI = KinematicsAPI(
z_lens,
z_source,
kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_seeing=kwargs_psf,
lens_model_kinematics_bool=[True, False, False, False, False],
anisotropy_model=anisotropy_model,
kwargs_mge_light=kwargs_mge,
kwargs_mge_mass=kwargs_mge,
sampling_number=1000,
MGE_light=True,
MGE_mass=True)
v_sigma_mge_lens = kinematicAPI.velocity_dispersion(kwargs_lens,
kwargs_lens_light,
kwargs_anisotropy,
r_eff=r_eff,
theta_E=theta_E)
#v_sigma_mge_lens = kinematicAPI.velocity_dispersion_numerical(kwargs_lens, kwargs_lens_light, kwargs_anisotropy, kwargs_aperture,
# kwargs_psf, anisotropy_model, MGE_light=True, MGE_mass=True, theta_E=theta_E,
# kwargs_mge_light=kwargs_mge, kwargs_mge_mass=kwargs_mge,
# r_eff=r_eff)
kinematicAPI = KinematicsAPI(
z_lens,
z_source,
kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_seeing=kwargs_psf,
lens_model_kinematics_bool=[True, False, False, False, False],
anisotropy_model=anisotropy_model,
kwargs_mge_light=kwargs_mge,
kwargs_mge_mass=kwargs_mge,
sampling_number=1000,
MGE_light=False,
MGE_mass=False,
Hernquist_approx=True)
v_sigma_hernquist = kinematicAPI.velocity_dispersion(kwargs_lens,
kwargs_lens_light,
kwargs_anisotropy,
r_eff=r_eff,
theta_E=theta_E)
#v_sigma_hernquist = kinematicAPI.velocity_dispersion_numerical(kwargs_lens, kwargs_lens_light, kwargs_anisotropy,
# kwargs_aperture, kwargs_psf, anisotropy_model,
# MGE_light=False, MGE_mass=False,
# r_eff=r_eff, Hernquist_approx=True)
vel_disp_temp = kinematicAPI.velocity_dispersion_analytical(
theta_E, gamma, r_ani=r_ani, r_eff=r_eff)
print(v_sigma, vel_disp_temp)
#assert 1 == 0
npt.assert_almost_equal(v_sigma / vel_disp_temp, 1, decimal=1)
npt.assert_almost_equal(v_sigma_mge_lens / v_sigma, 1, decimal=1)
npt.assert_almost_equal(v_sigma / v_sigma_hernquist, 1, decimal=1)
