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