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)