def test_PJaffa_density_deflection(self):
"""
tests whether the unit conversion between the lensing parameter 'sigma0' and the units in the density profile are ok
:return:
"""
from lenstronomy.LensModel.Profiles.p_jaffe import PJaffe as Model
lensModel = Model()
sigma0 = 1.
Ra = 0.2
Rs = 2.
rho0 = lensModel.sigma2rho(sigma0, Ra, Rs)
kwargs_lens = {
'sigma0': sigma0,
'Ra': Ra,
'Rs': Rs,
'center_x': 0,
'center_y': 0
}
kwargs_density = {'rho0': rho0, 'Ra': Ra, 'Rs': Rs}
r = 1.
mass_2d = lensModel.mass_2d(r, **kwargs_density)
alpha_mass = mass_2d / r
alpha_r, _ = lensModel.derivatives(r, 0, **kwargs_lens)
npt.assert_almost_equal(alpha_mass / np.pi, alpha_r, decimal=5)
def assert_lens_integrals(self, Model, kwargs):
"""
checks whether the integral in projection of the density_lens() function is the convergence
:param Model: lens model instance
:param kwargs: keyword arguments of lens model
:return:
"""
lensModel = Model()
int_profile = ProfileIntegrals(lensModel)
r = 2.
kappa_num = int_profile.density_2d(r, kwargs, lens_param=True)
f_xx, f_yy, f_xy = lensModel.hessian(r, 0, **kwargs)
kappa = 1./2 * (f_xx + f_yy)
npt.assert_almost_equal(kappa_num, kappa, decimal=2)
def assert_integrals(self, Model, kwargs):
lensModel = Model()
int_profile = ProfileIntegrals(lensModel)
r = 2.
density2d_num = int_profile.density_2d(r, kwargs)
density2d = lensModel.density_2d(r, 0, **kwargs)
npt.assert_almost_equal(density2d / density2d_num, 1., decimal=1)
mass_2d_num = int_profile.mass_enclosed_2d(r, kwargs)
mass_2d = lensModel.mass_2d(r, **kwargs)
npt.assert_almost_equal(mass_2d / mass_2d_num, 1, decimal=1)
mass_3d_num = int_profile.mass_enclosed_3d(r, kwargs)
mass_3d = lensModel.mass_3d(r, **kwargs)
npt.assert_almost_equal(mass_3d / mass_3d_num, 1, decimal=2)
def test_sie_density_deflection(self):
"""
tests whether the unit conversion between the lensing parameter 'sigma0' and the units in the density profile are ok
:return:
"""
from lenstronomy.LensModel.Profiles.sie import SIE as Model
lensModel = Model()
theta_E = 1.
rho0 = lensModel.theta2rho(theta_E)
kwargs_lens = {'theta_E': theta_E, 'e1': 0, 'e2': 0}
kwargs_density = {'rho0': rho0, 'e1': 0, 'e2': 0}
r = .5
mass_2d = lensModel.mass_2d(r, **kwargs_density)
alpha_mass = mass_2d / r
alpha_r, _ = lensModel.derivatives(r, 0, **kwargs_lens)
npt.assert_almost_equal(alpha_mass / np.pi, alpha_r, decimal=5)
def test_gaussian_density_deflection(self):
"""
tests whether the unit conversion between the lensing parameter 'sigma0' and the units in the density profile are ok
:return:
"""
from lenstronomy.LensModel.Profiles.gaussian_kappa import GaussianKappa as Model
lensModel = Model()
amp = 1. / 4.
sigma = 2.
amp_lens = lensModel._amp3d_to_2d(amp, sigma, sigma)
kwargs_lens = {'amp': amp_lens, 'sigma': sigma}
kwargs_density = {'amp': amp, 'sigma': sigma}
r = .5
mass_2d = lensModel.mass_2d(r, **kwargs_density)
alpha_mass = mass_2d / r
alpha_r, _ = lensModel.derivatives(r, 0, **kwargs_lens)
npt.assert_almost_equal(alpha_mass / np.pi, alpha_r, decimal=5)
def test_nfw_density_deflection(self):
"""
tests whether the unit conversion between the lensing parameter 'sigma0' and the units in the density profile are ok
:return:
"""
from lenstronomy.LensModel.Profiles.nfw import NFW as Model
lensModel = Model()
alpha_Rs = 1.
Rs = 2.
rho0 = lensModel.alpha2rho0(alpha_Rs, Rs)
kwargs_lens = {'alpha_Rs': alpha_Rs, 'Rs': Rs}
kwargs_density = {'rho0': rho0, 'Rs': Rs}
r = 2.
mass_2d = lensModel.mass_2d(r, **kwargs_density)
alpha_mass = mass_2d / r
alpha_r, _ = lensModel.derivatives(r, 0, **kwargs_lens)
npt.assert_almost_equal(alpha_mass / np.pi, alpha_r, decimal=5)
def assert_lens_integrals(self, Model, kwargs, pi_convention=True):
"""
checks whether the integral in projection of the density_lens() function is the convergence
:param Model: lens model instance
:param kwargs: keyword arguments of lens model
:return:
"""
lensModel = Model()
int_profile = ProfileIntegrals(lensModel)
r = 2.
kappa_num = int_profile.density_2d(r, kwargs, lens_param=True)
f_xx, f_xy, f_yx, f_yy = lensModel.hessian(r, 0, **kwargs)
kappa = 1. / 2 * (f_xx + f_yy)
npt.assert_almost_equal(kappa_num, kappa, decimal=2)
try:
del kwargs['center_x']
del kwargs['center_y']
except:
pass
bool_mass_2d_lens = False
try:
mass_2d = lensModel.mass_2d_lens(r, **kwargs)
bool_mass_2d_lens = True
except:
pass
if bool_mass_2d_lens:
alpha_x, alpha_y = lensModel.derivatives(r, 0, **kwargs)
alpha = np.sqrt(alpha_x**2 + alpha_y**2)
if pi_convention:
npt.assert_almost_equal(alpha, mass_2d / r / np.pi, decimal=5)
else:
npt.assert_almost_equal(alpha, mass_2d / r, decimal=5)
try:
mass_3d = lensModel.mass_3d_lens(r, **kwargs)
bool_mass_3d_lens = True
except:
bool_mass_3d_lens = False
if bool_mass_3d_lens:
mass_3d_num = int_profile.mass_enclosed_3d(r,
kwargs_profile=kwargs,
lens_param=True)
print(mass_3d, mass_3d_num, 'test num')
npt.assert_almost_equal(mass_3d / mass_3d_num, 1, decimal=2)
def assert_lens_integrals(self, Model, kwargs):
"""
checks whether the integral in projection of the density_lens() function is the convergence
:param Model: lens model instance
:param kwargs: keyword arguments of lens model
:return:
"""
lensModel = Model()
int_profile = ProfileIntegrals(lensModel)
r = 2.
kappa_num = int_profile.density_2d(r, kwargs, lens_param=True)
f_xx, f_yy, f_xy = lensModel.hessian(r, 0, **kwargs)
kappa = 1. / 2 * (f_xx + f_yy)
npt.assert_almost_equal(kappa_num, kappa, decimal=2)
if hasattr(lensModel, 'mass_2d_lens'):
mass_2d = lensModel.mass_2d_lens(r, **kwargs)
alpha_x, alpha_y = lensModel.derivatives(r, 0, **kwargs)
alpha = np.sqrt(alpha_x**2 + alpha_y**2)
npt.assert_almost_equal(alpha, mass_2d / r / np.pi, decimal=5)