def __init__(self,
kwargs_model,
kwargs_aperture_list,
kwargs_psf_list,
kwargs_cosmo,
kwargs_numerics=None,
analytic_kinematics=False):
"""
:param kwargs_model: keyword arguments describing the model components
:param kwargs_aperture_list: list of keyword arguments describing the spectroscopic aperture, see Aperture() class
:param kwargs_psf_list: list of keyword argument specifying the PSF of the observation
:param kwargs_cosmo: keyword arguments that define the cosmology in terms of the angular diameter distances involved
:param kwargs_numerics: numerics keyword arguments - see GalkinModel
:param analytic_kinematics: bool, if True uses the analytic kinematic model
"""
GalkinModel.__init__(self,
kwargs_model,
kwargs_cosmo,
kwargs_numerics=kwargs_numerics,
analytic_kinematics=analytic_kinematics)
self._observation_list = []
self._num_observations = len(kwargs_aperture_list)
for i in range(self._num_observations):
self._observation_list.append(
GalkinObservation(kwargs_aperture=kwargs_aperture_list[i],
kwargs_psf=kwargs_psf_list[i]))
def __init__(self,
kwargs_model,
kwargs_aperture,
kwargs_psf,
kwargs_cosmo,
kwargs_numerics=None,
analytic_kinematics=False):
"""
:param kwargs_model: keyword arguments describing the model components
:param kwargs_aperture: keyword arguments describing the spectroscopic aperture, see Aperture() class
:param kwargs_psf: keyword argument specifying the PSF of the observation
:param kwargs_cosmo: keyword arguments that define the cosmology in terms of the angular diameter distances
involved
:param kwargs_numerics: numerics keyword arguments
:param analytic_kinematics: bool, if True uses the analytic kinematic model
"""
GalkinModel.__init__(self,
kwargs_model,
kwargs_cosmo,
kwargs_numerics=kwargs_numerics,
analytic_kinematics=analytic_kinematics)
GalkinObservation.__init__(self,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf)
def test_radius_slope_anisotropy(self):
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
kwargs_model = {
'anisotropy_model': 'OM',
'mass_profile_list': ['SPP'],
'light_profile_list': ['HERNQUIST']
}
kwargs_numerics = {
'interpol_grid_num': 1000,
'log_integration': True,
'max_integrate': 100,
'min_integrate': 0.001
}
kin_analytic = GalkinModel(kwargs_model,
kwargs_cosmo,
analytic_kinematics=True,
kwargs_numerics=kwargs_numerics)
r = 1
theta_E, gamma = 1, 2.
a_ani = 10
r_eff = 0.1
out = kin_analytic.check_df(r,
kwargs_mass={
'theta_E': theta_E,
'gamma': gamma
},
kwargs_light={'r_eff': r_eff},
kwargs_anisotropy={'r_ani': a_ani * r_eff})
assert out > 0
print(out)
kin_numeric = GalkinModel(kwargs_model,
kwargs_cosmo,
analytic_kinematics=False,
kwargs_numerics=kwargs_numerics)
out_num = kin_numeric.check_df(
r,
kwargs_mass=[{
'theta_E': theta_E,
'gamma': gamma
}],
kwargs_light=[{
'Rs': r_eff * 0.551,
'amp': 1
}],
kwargs_anisotropy={'r_ani': a_ani * r_eff})
assert out_num > 1
npt.assert_almost_equal(out_num / out, 1, decimal=2)
kin_numeric_default = GalkinModel(kwargs_model,
kwargs_cosmo,
analytic_kinematics=False,
kwargs_numerics=None)