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_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)
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)
kwargs['center_x'] = 0
kwargs['center_y'] = 0
mass_2d_num = int_profile.mass_enclosed_2d(r, kwargs)
del kwargs['center_x']
del kwargs['center_y']
mass_2d = lensModel.mass_2d(r, **kwargs)
npt.assert_almost_equal(mass_2d / mass_2d_num, 1, decimal=1)
kwargs['center_x'] = 0
kwargs['center_y'] = 0
mass_3d_num = int_profile.mass_enclosed_3d(r, kwargs)
del kwargs['center_x']
del kwargs['center_y']
mass_3d = lensModel.mass_3d(r, **kwargs)
npt.assert_almost_equal(mass_3d / mass_3d_num, 1, decimal=2)