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)
class TestP_JAFFW(object): """ tests the Gaussian methods """ def setup(self): self.profile = PJaffe() def test_function(self): x = np.array([1]) y = np.array([2]) sigma0 = 1. Ra, Rs = 0.5, 0.8 values = self.profile.function(x, y, sigma0, Ra, Rs) assert values[0] == 0.87301557036070054 x = np.array([0]) y = np.array([0]) sigma0 = 1. Ra, Rs = 0.5, 0.8 values = self.profile.function(x, y, sigma0, Ra, Rs) assert values[0] == 0.20267440905756931 x = np.array([2, 3, 4]) y = np.array([1, 1, 1]) values = self.profile.function(x, y, sigma0, Ra, Rs) assert values[0] == 0.87301557036070054 assert values[1] == 1.0842781309377669 assert values[2] == 1.2588604178849985 def test_derivatives(self): x = np.array([1]) y = np.array([2]) sigma0 = 1. Ra, Rs = 0.5, 0.8 f_x, f_y = self.profile.derivatives(x, y, sigma0, Ra, Rs) assert f_x[0] == 0.11542369603751264 assert f_y[0] == 0.23084739207502528 x = np.array([0]) y = np.array([0]) f_x, f_y = self.profile.derivatives(x, y, sigma0, Ra, Rs) assert f_x[0] == 0 assert f_y[0] == 0 x = np.array([1, 3, 4]) y = np.array([2, 1, 1]) values = self.profile.derivatives(x, y, sigma0, Ra, Rs) assert values[0][0] == 0.11542369603751264 assert values[1][0] == 0.23084739207502528 assert values[0][1] == 0.19172866612512479 assert values[1][1] == 0.063909555375041588 def test_hessian(self): x = np.array([1]) y = np.array([2]) sigma0 = 1. Ra, Rs = 0.5, 0.8 f_xx, f_yy, f_xy = self.profile.hessian(x, y, sigma0, Ra, Rs) assert f_xx[0] == 0.077446121589827679 assert f_yy[0] == -0.036486601753227141 assert f_xy[0] == -0.075955148895369876 x = np.array([1, 3, 4]) y = np.array([2, 1, 1]) values = self.profile.hessian(x, y, sigma0, Ra, Rs) assert values[0][0] == 0.077446121589827679 assert values[1][0] == -0.036486601753227141 assert values[2][0] == -0.075955148895369876 assert values[0][1] == -0.037260794616683197 assert values[1][1] == 0.052668405375961035 assert values[2][1] == -0.033723449997241584 def test_mass_tot(self): rho0 = 1. Ra, Rs = 0.5, 0.8 values = self.profile.mass_tot(rho0, Ra, Rs) npt.assert_almost_equal(values, 2.429441083345073, decimal=10) def test_mass_3d_lens(self): mass = self.profile.mass_3d_lens(r=1, sigma0=1, Ra=0.5, Rs=0.8) npt.assert_almost_equal(mass, 0.87077306005349242, decimal=8) def test_grav_pot(self): x = 1 y = 2 rho0 = 1. Ra, Rs = 0.5, 0.8 grav_pot = self.profile.grav_pot(x, y, rho0, Ra, Rs, center_x=0, center_y=0) npt.assert_almost_equal(grav_pot, 0.89106542283974155, decimal=10)
class TestP_JAFFW(object): """ tests the Gaussian methods """ def setup(self): self.profile = PJaffe() def test_function(self): x = np.array([1]) y = np.array([2]) sigma0 = 1. Ra, Rs = 0.5, 0.8 values = self.profile.function(x, y, sigma0, Ra, Rs) npt.assert_almost_equal(values[0], 0.87301557036070054, decimal=8) x = np.array([0]) y = np.array([0]) sigma0 = 1. Ra, Rs = 0.5, 0.8 values = self.profile.function(x, y, sigma0, Ra, Rs) npt.assert_almost_equal(values[0], 0.20267440905756931, decimal=8) x = np.array([2, 3, 4]) y = np.array([1, 1, 1]) values = self.profile.function(x, y, sigma0, Ra, Rs) npt.assert_almost_equal(values[0], 0.87301557036070054, decimal=8) npt.assert_almost_equal(values[1], 1.0842781309377669, decimal=8) npt.assert_almost_equal(values[2], 1.2588604178849985, decimal=8) def test_derivatives(self): x = np.array([1]) y = np.array([2]) sigma0 = 1. Ra, Rs = 0.5, 0.8 f_x, f_y = self.profile.derivatives(x, y, sigma0, Ra, Rs) npt.assert_almost_equal(f_x[0], 0.11542369603751264, decimal=8) npt.assert_almost_equal(f_y[0], 0.23084739207502528, decimal=8) x = np.array([0]) y = np.array([0]) f_x, f_y = self.profile.derivatives(x, y, sigma0, Ra, Rs) assert f_x[0] == 0 assert f_y[0] == 0 x = np.array([1, 3, 4]) y = np.array([2, 1, 1]) values = self.profile.derivatives(x, y, sigma0, Ra, Rs) npt.assert_almost_equal(values[0][0], 0.11542369603751264, decimal=8) npt.assert_almost_equal(values[1][0], 0.23084739207502528, decimal=8) npt.assert_almost_equal(values[0][1], 0.19172866612512479, decimal=8) npt.assert_almost_equal(values[1][1], 0.063909555375041588, decimal=8) def test_hessian(self): x = np.array([1]) y = np.array([2]) sigma0 = 1. Ra, Rs = 0.5, 0.8 f_xx, f_xy, f_yx, f_yy = self.profile.hessian(x, y, sigma0, Ra, Rs) npt.assert_almost_equal(f_xx[0], 0.077446121589827679, decimal=8) npt.assert_almost_equal(f_yy[0], -0.036486601753227141, decimal=8) npt.assert_almost_equal(f_xy[0], -0.075955148895369876, decimal=8) x = np.array([1, 3, 4]) y = np.array([2, 1, 1]) values = self.profile.hessian(x, y, sigma0, Ra, Rs) npt.assert_almost_equal(values[0][0], 0.077446121589827679, decimal=8) npt.assert_almost_equal(values[3][0], -0.036486601753227141, decimal=8) npt.assert_almost_equal(values[1][0], values[2][0], decimal=8) def test_mass_tot(self): rho0 = 1. Ra, Rs = 0.5, 0.8 values = self.profile.mass_tot(rho0, Ra, Rs) npt.assert_almost_equal(values, 2.429441083345073, decimal=10) def test_mass_3d_lens(self): mass = self.profile.mass_3d_lens(r=1, sigma0=1, Ra=0.5, Rs=0.8) npt.assert_almost_equal(mass, 0.87077306005349242, decimal=8) def test_grav_pot(self): x = 1 y = 2 rho0 = 1. r = np.sqrt(x**2 + y**2) Ra, Rs = 0.5, 0.8 grav_pot = self.profile.grav_pot(r, rho0, Ra, Rs) npt.assert_almost_equal(grav_pot, 0.89106542283974155, decimal=10)