class TestSIE(object):
"""
tests the Gaussian methods
"""
def setup(self):
from lenstronomy.LensModel.Profiles.sie import SIE
from lenstronomy.LensModel.Profiles.spemd import SPEMD
self.sie = SIE()
self.spemd = SPEMD()
def test_function(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.9
phi_G = 1.
values = self.sie.function(x, y, theta_E, q, phi_G)
gamma = 2
values_spemd = self.spemd.function(x, y, theta_E, gamma, q, phi_G)
assert values == values_spemd
def test_derivatives(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.9
phi_G = 1.
values = self.sie.derivatives(x, y, theta_E, q, phi_G)
gamma = 2
values_spemd = self.spemd.derivatives(x, y, theta_E, gamma, q, phi_G)
assert values == values_spemd
def test_hessian(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.9
phi_G = 1.
values = self.sie.hessian(x, y, theta_E, q, phi_G)
gamma = 2
values_spemd = self.spemd.hessian(x, y, theta_E, gamma, q, phi_G)
assert values[0] == values_spemd[0]
class CurvedArcTanDiff(LensProfileBase):
"""
Curved arc model with an additional non-zero tangential stretch differential in tangential direction component
Observables are:
- curvature radius (basically bending relative to the center of the profile)
- radial stretch (plus sign) thickness of arc with parity (more generalized than the power-law slope)
- tangential stretch (plus sign). Infinity means at critical curve
- direction of curvature
- position of arc
Requirements:
- Should work with other perturbative models without breaking its meaning (say when adding additional shear terms)
- Must best reflect the observables in lensing
- minimal covariances between the parameters, intuitive parameterization.
"""
param_names = [
'tangential_stretch', 'radial_stretch', 'curvature', 'dtan_dtan',
'direction', 'center_x', 'center_y'
]
lower_limit_default = {
'tangential_stretch': -100,
'radial_stretch': -5,
'curvature': 0.000001,
'dtan_dtan': -10,
'direction': -np.pi,
'center_x': -100,
'center_y': -100
}
upper_limit_default = {
'tangential_stretch': 100,
'radial_stretch': 5,
'curvature': 100,
'dtan_dtan': 10,
'direction': np.pi,
'center_x': 100,
'center_y': 100
}
def __init__(self):
self._sie = SIE(NIE=True)
self._mst = Convergence()
super(CurvedArcTanDiff, self).__init__()
@staticmethod
def stretch2sie_mst(tangential_stretch, radial_stretch, curvature,
dtan_dtan, direction, center_x, center_y):
"""
:param tangential_stretch: float, stretch of intrinsic source in tangential direction
:param radial_stretch: float, stretch of intrinsic source in radial direction
:param curvature: 1/curvature radius
:param dtan_dtan: d(tangential_stretch) / d(tangential direction) / tangential stretch
:param direction: float, angle in radian
:param center_x: center of source in image plane
:param center_y: center of source in image plane
:return: parameters in terms of a spherical SIS + MST resulting in the same observables
"""
center_x_sis, center_y_sis = center_deflector(curvature, direction,
center_x, center_y)
r_curvature = 1. / curvature
lambda_mst = 1. / radial_stretch
kappa_ext = 1 - lambda_mst
theta_E = r_curvature * (1. - radial_stretch / tangential_stretch)
# analytic relation (see Birrer 2021)
dlambda_tan_dr = tangential_stretch / r_curvature * (
1 - tangential_stretch / radial_stretch)
# translate tangential eigenvalue gradient in lens ellipticity
dtan_dtan_ = dtan_dtan * tangential_stretch
epsilon = np.abs(dtan_dtan_ / dlambda_tan_dr)
# bound epsilon by (-1, 1)
epsilon = np.minimum(epsilon, 0.99999)
q = np.sqrt((1 - epsilon) / (1 + epsilon))
if dtan_dtan_ < 0:
phi = direction - np.pi / 4
else:
phi = direction + np.pi / 4
e1_sie, e2_sie = param_util.phi_q2_ellipticity(phi, q)
# ellipticity adopted Einstein radius to match local tangential and radial stretch
factor = np.sqrt(1 + q**2) / np.sqrt(2 * q)
theta_E_sie = theta_E * factor
return theta_E_sie, e1_sie, e2_sie, kappa_ext, center_x_sis, center_y_sis
def function(self, x, y, tangential_stretch, radial_stretch, curvature,
dtan_dtan, direction, center_x, center_y):
"""
ATTENTION: there may not be a global lensing potential!
:param x:
:param y:
:param tangential_stretch: float, stretch of intrinsic source in tangential direction
:param radial_stretch: float, stretch of intrinsic source in radial direction
:param curvature: 1/curvature radius
:param direction: float, angle in radian
:param dtan_dtan: d(tangential_stretch) / d(tangential direction) / tangential stretch
:param center_x: center of source in image plane
:param center_y: center of source in image plane
:return:
"""
lambda_mst = 1. / radial_stretch
theta_E_sie, e1_sie, e2_sie, kappa_ext, center_x_sis, center_y_sis = self.stretch2sie_mst(
tangential_stretch, radial_stretch, curvature, dtan_dtan,
direction, center_x, center_y)
f_sis = self._sie.function(
x, y, theta_E_sie, e1_sie, e2_sie, center_x_sis, center_y_sis
) # - self._sis.function(center_x, center_y, theta_E, center_x_sis, center_y_sis)
alpha_x, alpha_y = self._sie.derivatives(center_x, center_y,
theta_E_sie, e1_sie, e2_sie,
center_x_sis, center_y_sis)
f_sis_0 = alpha_x * (x - center_x) + alpha_y * (y - center_y)
f_mst = self._mst.function(x,
y,
kappa_ext,
ra_0=center_x,
dec_0=center_y)
return lambda_mst * (f_sis - f_sis_0) + f_mst
def derivatives(self, x, y, tangential_stretch, radial_stretch, curvature,
dtan_dtan, direction, center_x, center_y):
"""
:param x:
:param y:
:param tangential_stretch: float, stretch of intrinsic source in tangential direction
:param radial_stretch: float, stretch of intrinsic source in radial direction
:param curvature: 1/curvature radius
:param direction: float, angle in radian
:param dtan_dtan: d(tangential_stretch) / d(tangential direction) / tangential stretch
:param center_x: center of source in image plane
:param center_y: center of source in image plane
:return:
"""
lambda_mst = 1. / radial_stretch
theta_E_sie, e1_sie, e2_sie, kappa_ext, center_x_sis, center_y_sis = self.stretch2sie_mst(
tangential_stretch, radial_stretch, curvature, dtan_dtan,
direction, center_x, center_y)
f_x_sis, f_y_sis = self._sie.derivatives(x, y, theta_E_sie, e1_sie,
e2_sie, center_x_sis,
center_y_sis)
f_x0, f_y0 = self._sie.derivatives(center_x, center_y, theta_E_sie,
e1_sie, e2_sie, center_x_sis,
center_y_sis)
f_x_mst, f_y_mst = self._mst.derivatives(x,
y,
kappa_ext,
ra_0=center_x,
dec_0=center_y)
f_x = lambda_mst * (f_x_sis - f_x0) + f_x_mst
f_y = lambda_mst * (f_y_sis - f_y0) + f_y_mst
return f_x, f_y
def hessian(self, x, y, tangential_stretch, radial_stretch, curvature,
dtan_dtan, direction, center_x, center_y):
"""
:param x:
:param y:
:param tangential_stretch: float, stretch of intrinsic source in tangential direction
:param radial_stretch: float, stretch of intrinsic source in radial direction
:param curvature: 1/curvature radius
:param direction: float, angle in radian
:param dtan_dtan: d(tangential_stretch) / d(tangential direction) / tangential stretch
:param center_x: center of source in image plane
:param center_y: center of source in image plane
:return:
"""
lambda_mst = 1. / radial_stretch
theta_E_sie, e1_sie, e2_sie, kappa_ext, center_x_sis, center_y_sis = self.stretch2sie_mst(
tangential_stretch, radial_stretch, curvature, dtan_dtan,
direction, center_x, center_y)
f_xx_sis, f_xy_sis, f_yx_sis, f_yy_sis = self._sie.hessian(
x, y, theta_E_sie, e1_sie, e2_sie, center_x_sis, center_y_sis)
f_xx_mst, f_xy_mst, f_yx_mst, f_yy_mst = self._mst.hessian(
x, y, kappa_ext, ra_0=center_x, dec_0=center_y)
return lambda_mst * f_xx_sis + f_xx_mst, lambda_mst * f_xy_sis + f_xy_mst, lambda_mst * f_yx_sis + f_yx_mst, lambda_mst * f_yy_sis + f_yy_mst
class TestSIE(object):
"""
tests the Gaussian methods
"""
def setup(self):
from lenstronomy.LensModel.Profiles.sie import SIE
from lenstronomy.LensModel.Profiles.spemd import SPEMD
from lenstronomy.LensModel.Profiles.nie import NIE
self.sie = SIE(NIE=False)
self.sie_nie = SIE(NIE=True)
self.spemd = SPEMD()
self.nie = NIE()
def test_function(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.9
phi_G = 1.
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
values = self.sie.function(x, y, theta_E, e1, e2)
gamma = 2
values_spemd = self.spemd.function(x, y, theta_E, gamma, e1, e2)
assert values == values_spemd
values = self.sie_nie.function(x, y, theta_E, e1, e2)
s_scale = 0.0000001
values_spemd = self.nie.function(x, y, theta_E, e1, e2, s_scale)
npt.assert_almost_equal(values, values_spemd, decimal=6)
def test_derivatives(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.9
phi_G = 1.
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
values = self.sie.derivatives(x, y, theta_E, e1, e2)
gamma = 2
values_spemd = self.spemd.derivatives(x, y, theta_E, gamma, e1, e2)
assert values == values_spemd
values = self.sie_nie.derivatives(x, y, theta_E, e1, e2)
s_scale = 0.0000001
values_spemd = self.nie.derivatives(x, y, theta_E, e1, e2, s_scale)
npt.assert_almost_equal(values, values_spemd, decimal=6)
def test_hessian(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.9
phi_G = 1.
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
values = self.sie.hessian(x, y, theta_E, e1, e2)
gamma = 2
values_spemd = self.spemd.hessian(x, y, theta_E, gamma, e1, e2)
assert values[0] == values_spemd[0]
values = self.sie_nie.hessian(x, y, theta_E, e1, e2)
s_scale = 0.0000001
values_spemd = self.nie.hessian(x, y, theta_E, e1, e2, s_scale)
npt.assert_almost_equal(values, values_spemd, decimal=5)