class TestEPLvsNIE(object): """ tests the Gaussian methods """ def setup(self): from lenstronomy.LensModel.Profiles.epl import EPL self.EPL = EPL() from lenstronomy.LensModel.Profiles.nie import NIE self.NIE = NIE() def test_function(self): phi_E = 1. gamma = 2. q = 0.999 phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) x = np.array([1., 2]) y = np.array([2, 0]) values = self.EPL.function(x, y, phi_E, gamma, e1, e2) values_nie = self.NIE.function(x, y, phi_E, e1, e2, 0.) delta_f = values[0] - values[1] delta_f_nie = values_nie[0] - values_nie[1] npt.assert_almost_equal(delta_f, delta_f_nie, decimal=5) q = 0.8 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) x = np.array([1., 2]) y = np.array([2, 0]) values = self.EPL.function(x, y, phi_E, gamma, e1, e2) values_nie = self.NIE.function(x, y, phi_E, e1, e2, 0.) delta_f = values[0] - values[1] delta_f_nie = values_nie[0] - values_nie[1] npt.assert_almost_equal(delta_f, delta_f_nie, decimal=5) q = 0.4 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) x = np.array([1., 2]) y = np.array([2, 0]) values = self.EPL.function(x, y, phi_E, gamma, e1, e2) values_nie = self.NIE.function(x, y, phi_E, e1, e2, 0.) delta_f = values[0] - values[1] delta_f_nie = values_nie[0] - values_nie[1] npt.assert_almost_equal(delta_f, delta_f_nie, decimal=5) def test_derivatives(self): x = np.array([1]) y = np.array([2]) phi_E = 1. gamma = 2. q = 1. phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_x, f_y = self.EPL.derivatives(x, y, phi_E, gamma, e1, e2) f_x_nie, f_y_nie = self.NIE.derivatives(x, y, phi_E, e1, e2, 0.) npt.assert_almost_equal(f_x, f_x_nie, decimal=4) npt.assert_almost_equal(f_y, f_y_nie, decimal=4) q = 0.7 phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_x, f_y = self.EPL.derivatives(x, y, phi_E, gamma, e1, e2) f_x_nie, f_y_nie = self.NIE.derivatives(x, y, phi_E, e1, e2, 0.) npt.assert_almost_equal(f_x, f_x_nie, decimal=4) npt.assert_almost_equal(f_y, f_y_nie, decimal=4) def test_hessian(self): x = np.array([1.]) y = np.array([2.]) phi_E = 1. gamma = 2. q = 0.9 phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_xx, f_xy, f_yx, f_yy = self.EPL.hessian(x, y, phi_E, gamma, e1, e2) f_xx_nie, f_xy_nie, f_yx_nie, f_yy_nie = self.NIE.hessian(x, y, phi_E, e1, e2, 0.) npt.assert_almost_equal(f_xx, f_xx_nie, decimal=4) npt.assert_almost_equal(f_yy, f_yy_nie, decimal=4) npt.assert_almost_equal(f_xy, f_xy_nie, decimal=4) npt.assert_almost_equal(f_xy, f_yx, decimal=8) def test_density_lens(self): r = 1 kwargs = {'theta_E': 1, 'gamma': 2, 'e1': 0, 'e2': 0} rho = self.EPL.density_lens(r, **kwargs) from lenstronomy.LensModel.Profiles.spep import SPEP spep = SPEP() rho_spep = spep.density_lens(r, **kwargs) npt.assert_almost_equal(rho, rho_spep, decimal=7) def test_mass_3d_lens(self): r = 1 kwargs = {'theta_E': 1, 'gamma': 2, 'e1': 0, 'e2': 0} rho = self.EPL.mass_3d_lens(r, **kwargs) from lenstronomy.LensModel.Profiles.spep import SPEP spep = SPEP() rho_spep = spep.mass_3d_lens(r, **kwargs) npt.assert_almost_equal(rho, rho_spep, decimal=7) def test_static(self): x, y = 1., 1. phi_G, q = 0.3, 0.8 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) kwargs_lens = {'theta_E': 1., 'gamma': 1.5, 'e1': e1, 'e2': e2} f_ = self.EPL.function(x, y, **kwargs_lens) self.EPL.set_static(**kwargs_lens) f_static = self.EPL.function(x, y, **kwargs_lens) npt.assert_almost_equal(f_, f_static, decimal=8) self.EPL.set_dynamic() kwargs_lens = {'theta_E': 2., 'gamma': 1.9, 'e1': e1, 'e2': e2} f_dyn = self.EPL.function(x, y, **kwargs_lens) assert f_dyn != f_static def test_regularization(self): phi_E = 1. gamma = 2. q = 1. phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) x = 0. y = 0. f_x, f_y = self.EPL.derivatives(x, y, phi_E, gamma, e1, e2) npt.assert_almost_equal(f_x, 0.) npt.assert_almost_equal(f_y, 0.) x = 0. y = 0. f_xx, f_xy, f_yx, f_yy = self.EPL.hessian(x, y, phi_E, gamma, e1, e2) assert f_xx > 10 ** 5 assert f_yy > 10 ** 5 #npt.assert_almost_equal(f_xx, 10**10) #npt.assert_almost_equal(f_yy, 10**10) npt.assert_almost_equal(f_xy, 0) npt.assert_almost_equal(f_yx, 0)
class TestEPL(object): """ tests the Gaussian methods """ def setup(self): from lenstronomy.LensModel.Profiles.epl import EPL self.EPL = EPL() from lenstronomy.LensModel.Profiles.nie import NIE self.NIE = NIE() def test_function(self): phi_E = 1. t = 1. q = 0.999 phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) x = np.array([1., 2]) y = np.array([2, 0]) values = self.EPL.function(x, y, phi_E, e1, e2, t) values_nie = self.NIE.function(x, y, phi_E, e1, e2, 0.) delta_f = values[0] - values[1] delta_f_nie = values_nie[0] - values_nie[1] npt.assert_almost_equal(delta_f, delta_f_nie, decimal=5) q = 0.8 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) x = np.array([1., 2]) y = np.array([2, 0]) values = self.EPL.function(x, y, phi_E, e1, e2, t) values_nie = self.NIE.function(x, y, phi_E, e1, e2, 0.) delta_f = values[0] - values[1] delta_f_nie = values_nie[0] - values_nie[1] npt.assert_almost_equal(delta_f, delta_f_nie, decimal=5) q = 0.4 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) x = np.array([1., 2]) y = np.array([2, 0]) values = self.EPL.function(x, y, phi_E, e1, e2, t) values_nie = self.NIE.function(x, y, phi_E, e1, e2, 0.) delta_f = values[0] - values[1] delta_f_nie = values_nie[0] - values_nie[1] npt.assert_almost_equal(delta_f, delta_f_nie, decimal=5) def test_derivatives(self): x = np.array([1]) y = np.array([2]) phi_E = 1. t = 1. q = 1. phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_x, f_y = self.EPL.derivatives(x, y, phi_E, e1, e2, t) f_x_nie, f_y_nie = self.NIE.derivatives(x, y, phi_E, e1, e2, 0.) npt.assert_almost_equal(f_x, f_x_nie, decimal=4) npt.assert_almost_equal(f_y, f_y_nie, decimal=4) q = 0.7 phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_x, f_y = self.EPL.derivatives(x, y, phi_E, e1, e2, t) f_x_nie, f_y_nie = self.NIE.derivatives(x, y, phi_E, e1, e2, 0.) npt.assert_almost_equal(f_x, f_x_nie, decimal=4) npt.assert_almost_equal(f_y, f_y_nie, decimal=4) def test_hessian(self): x = np.array([1.]) y = np.array([2.]) phi_E = 1. t = 1. q = 0.9 phi_G = 1. e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) f_xx, f_yy, f_xy = self.EPL.hessian(x, y, phi_E, e1, e2, t) f_xx_nie, f_yy_nie, f_xy_nie = self.NIE.hessian( x, y, phi_E, e1, e2, 0.) npt.assert_almost_equal(f_xx, f_xx_nie, decimal=4) npt.assert_almost_equal(f_yy, f_yy_nie, decimal=4) npt.assert_almost_equal(f_xy, f_xy_nie, decimal=4) def test_static(self): x, y = 1., 1. phi_G, q = 0.3, 0.8 e1, e2 = param_util.phi_q2_ellipticity(phi_G, q) kwargs_lens = {'theta_E': 1., 't': 1.5, 'e1': e1, 'e2': e2} f_ = self.EPL.function(x, y, **kwargs_lens) self.EPL.set_static(**kwargs_lens) f_static = self.EPL.function(x, y, **kwargs_lens) npt.assert_almost_equal(f_, f_static, decimal=8) self.EPL.set_dynamic() kwargs_lens = {'theta_E': 2., 't': 0.5, 'e1': e1, 'e2': e2} f_dyn = self.EPL.function(x, y, **kwargs_lens) assert f_dyn != f_static