class TestConvergence(object):
"""
tests the Gaussian methods
"""
def setup(self):
self.profile = Convergence()
self.kwargs_lens = {'kappa_ext': 0.1}
def test_function(self):
x = np.array([1])
y = np.array([0])
values = self.profile.function(x, y, **self.kwargs_lens)
npt.assert_almost_equal(values[0],
self.kwargs_lens['kappa_ext'] / 2,
decimal=5)
x = np.array([0])
y = np.array([0])
values = self.profile.function(x, y, **self.kwargs_lens)
npt.assert_almost_equal(values[0], 0, decimal=5)
x = np.array([2, 3, 4])
y = np.array([1, 1, 1])
values = self.profile.function(x, y, **self.kwargs_lens)
npt.assert_almost_equal(values[0], 0.25, decimal=5)
npt.assert_almost_equal(values[1], 0.5, decimal=5)
def test_derivatives(self):
x = np.array([1])
y = np.array([2])
f_x, f_y = self.profile.derivatives(x, y, **self.kwargs_lens)
npt.assert_almost_equal(f_x[0], 0.1, decimal=5)
npt.assert_almost_equal(f_y[0], 0.2, decimal=5)
x = np.array([1, 3, 4])
y = np.array([2, 1, 1])
values = self.profile.derivatives(x, y, **self.kwargs_lens)
npt.assert_almost_equal(values[0][0], 0.1, decimal=5)
npt.assert_almost_equal(values[1][0], 0.2, decimal=5)
def test_hessian(self):
x = np.array([1])
y = np.array([2])
f_xx, f_xy, f_yx, f_yy = self.profile.hessian(x, y, **self.kwargs_lens)
npt.assert_almost_equal(f_xx, 0.1, decimal=5)
npt.assert_almost_equal(f_yy, 0.1, decimal=5)
npt.assert_almost_equal(f_xy, 0, decimal=5)
npt.assert_almost_equal(f_yx, 0, decimal=5)
x = np.array([1, 3, 4])
y = np.array([2, 1, 1])
values = self.profile.hessian(x, y, **self.kwargs_lens)
npt.assert_almost_equal(values[0], 0.1, decimal=5)
npt.assert_almost_equal(values[3], 0.1, decimal=5)
npt.assert_almost_equal(values[1], 0, decimal=5)
class CurvedArcConstMST(LensProfileBase):
"""
lens model that describes a section of a highly magnified deflector region.
The parameterization is chosen to describe local observables efficient.
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', 'direction',
'center_x', 'center_y'
]
lower_limit_default = {
'tangential_stretch': -100,
'radial_stretch': -5,
'curvature': 0.000001,
'direction': -np.pi,
'center_x': -100,
'center_y': -100
}
upper_limit_default = {
'tangential_stretch': 100,
'radial_stretch': 5,
'curvature': 100,
'direction': np.pi,
'center_x': 100,
'center_y': 100
}
def __init__(self):
self._mst = Convergence()
self._curve = CurvedArcConst()
super(CurvedArcConstMST, self).__init__()
def function(self, x, y, tangential_stretch, radial_stretch, curvature,
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 center_x: center of source in image plane
:param center_y: center of source in image plane
:return:
"""
raise NotImplemented(
'lensing potential for regularly curved arc is not implemented')
def derivatives(self, x, y, tangential_stretch, radial_stretch, curvature,
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 center_x: center of source in image plane
:param center_y: center of source in image plane
:return:
"""
lambda_mst = 1. / radial_stretch
kappa_ext = 1 - lambda_mst
curve_stretch = tangential_stretch / radial_stretch
f_x_curve, f_y_curve = self._curve.derivatives(x, y, curve_stretch,
curvature, direction,
center_x, center_y)
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_curve + f_x_mst
f_y = lambda_mst * f_y_curve + f_y_mst
return f_x, f_y
def hessian(self, x, y, tangential_stretch, radial_stretch, curvature,
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 center_x: center of source in image plane
:param center_y: center of source in image plane
:return:
"""
lambda_mst = 1. / radial_stretch
kappa_ext = 1 - lambda_mst
curve_stretch = tangential_stretch / radial_stretch
f_xx_c, f_xy_c, f_yx_c, f_yy_c = self._curve.hessian(
x, y, curve_stretch, curvature, direction, center_x, center_y)
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)
f_xx = lambda_mst * f_xx_c + f_xx_mst
f_xy = lambda_mst * f_xy_c + f_xy_mst
f_yx = lambda_mst * f_yx_c + f_yx_mst
f_yy = lambda_mst * f_yy_c + f_yy_mst
return f_xx, f_xy, f_yx, f_yy
class TestCurvedArcSISMST(object):
"""
tests the source model routines
"""
def setup(self):
self.model = CurvedArcSISMST()
self.sis = SIS()
self.mst = Convergence()
def test_spp2stretch(self):
center_x, center_y = 1, 1
theta_E = 1
kappa = 0.1
center_x_spp, center_y_spp = 0., 0
tangential_stretch, radial_stretch, curvature, direction = self.model.sis_mst2stretch(theta_E, kappa, center_x_spp, center_y_spp, center_x, center_y)
theta_E_new, kappa_new, center_x_spp_new, center_y_spp_new = self.model.stretch2sis_mst(tangential_stretch, radial_stretch, curvature, direction, center_x, center_y)
npt.assert_almost_equal(center_x_spp_new, center_x_spp, decimal=8)
npt.assert_almost_equal(center_y_spp_new, center_y_spp, decimal=8)
npt.assert_almost_equal(theta_E_new, theta_E, decimal=8)
npt.assert_almost_equal(kappa_new, kappa, decimal=8)
center_x, center_y = -1, 1
tangential_stretch, radial_stretch, curvature, direction = self.model.sis_mst2stretch(theta_E, kappa,
center_x_spp, center_y_spp,
center_x, center_y)
theta_E_new, kappa_new, center_x_spp_new, center_y_spp_new = self.model.stretch2sis_mst(tangential_stretch,
radial_stretch, curvature,
direction, center_x,
center_y)
npt.assert_almost_equal(center_x_spp_new, center_x_spp, decimal=8)
npt.assert_almost_equal(center_y_spp_new, center_y_spp, decimal=8)
npt.assert_almost_equal(theta_E_new, theta_E, decimal=8)
npt.assert_almost_equal(kappa_new, kappa, decimal=8)
center_x, center_y = 0, 0.5
tangential_stretch, radial_stretch, curvature, direction = self.model.sis_mst2stretch(theta_E, kappa,
center_x_spp, center_y_spp,
center_x, center_y)
theta_E_new, kappa_new, center_x_spp_new, center_y_spp_new = self.model.stretch2sis_mst(tangential_stretch,
radial_stretch, curvature,
direction, center_x,
center_y)
npt.assert_almost_equal(center_x_spp_new, center_x_spp, decimal=8)
npt.assert_almost_equal(center_y_spp_new, center_y_spp, decimal=8)
npt.assert_almost_equal(theta_E_new, theta_E, decimal=8)
npt.assert_almost_equal(kappa_new, kappa, decimal=8)
center_x, center_y = 0, -1.5
tangential_stretch, radial_stretch, r_curvature, direction = self.model.sis_mst2stretch(theta_E, kappa,
center_x_spp, center_y_spp,
center_x, center_y)
print(tangential_stretch, radial_stretch, r_curvature, direction)
theta_E_new, kappa_new, center_x_spp_new, center_y_spp_new = self.model.stretch2sis_mst(tangential_stretch,
radial_stretch, r_curvature,
direction, center_x,
center_y)
npt.assert_almost_equal(center_x_spp_new, center_x_spp, decimal=8)
npt.assert_almost_equal(center_y_spp_new, center_y_spp, decimal=8)
npt.assert_almost_equal(theta_E_new, theta_E, decimal=8)
npt.assert_almost_equal(kappa_new, kappa, decimal=8)
def test_function(self):
center_x, center_y = 0., 0.
x, y = 1, 1
radial_stretch = 1
output = self.model.function(x, y, tangential_stretch=2, radial_stretch=radial_stretch, curvature=1./2, direction=0, center_x=center_x, center_y=center_y)
theta_E, kappa_ext, center_x_sis, center_y_sis = self.model.stretch2sis_mst(tangential_stretch=2, radial_stretch=radial_stretch, curvature=1./2, direction=0,
center_x=center_x, center_y=center_y)
f_sis_out = self.sis.function(1, 1, theta_E, center_x_sis, center_y_sis) # - self.sis.function(0, 0, theta_E, center_x_sis, center_y_sis)
alpha_x, alpha_y = self.sis.derivatives(center_x, center_y, theta_E, center_x_sis, center_y_sis)
f_sis_0_out = alpha_x * (x - center_x) + alpha_y * (y - center_y)
f_mst_out = self.mst.function(x, y, kappa_ext, ra_0=center_x, dec_0=center_y)
lambda_mst = 1. / radial_stretch
f_out = lambda_mst * (f_sis_out - f_sis_0_out) + f_mst_out
npt.assert_almost_equal(output, f_out, decimal=8)
def test_derivatives(self):
tangential_stretch = 5
radial_stretch = 1
curvature = 1./10
direction = 0.3
center_x = 0
center_y = 0
x, y = 1, 1
theta_E, kappa, center_x_spp, center_y_spp = self.model.stretch2sis_mst(tangential_stretch,
radial_stretch, curvature,
direction, center_x, center_y)
f_x_sis, f_y_sis = self.sis.derivatives(x, y, theta_E, center_x_spp, center_y_spp)
f_x_mst, f_y_mst = self.mst.derivatives(x, y, kappa, ra_0=center_x, dec_0=center_y)
f_x0, f_y0 = self.sis.derivatives(center_x, center_y, theta_E, center_x_spp, center_y_spp)
f_x_new, f_y_new = self.model.derivatives(x, y, tangential_stretch, radial_stretch, curvature, direction, center_x, center_y)
npt.assert_almost_equal(f_x_new, f_x_sis + f_x_mst - f_x0, decimal=8)
npt.assert_almost_equal(f_y_new, f_y_sis + f_y_mst - f_y0, decimal=8)
def test_hessian(self):
lens = LensModel(lens_model_list=['CURVED_ARC_SIS_MST'])
center_x, center_y = 0, 0
tangential_stretch = 10
radial_stretch = 1
kwargs_lens = [
{'tangential_stretch': tangential_stretch, 'radial_stretch': radial_stretch, 'curvature': 1./10.5, 'direction': 0.,
'center_x': center_x, 'center_y': center_y}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag, tangential_stretch * radial_stretch, decimal=8)
center_x, center_y = 2, 3
tangential_stretch = 10
radial_stretch = 1
kwargs_lens = [
{'tangential_stretch': tangential_stretch, 'radial_stretch': radial_stretch, 'curvature': 1./10.5, 'direction': 0.,
'center_x': center_x, 'center_y': center_y}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag, tangential_stretch * radial_stretch, decimal=8)
center_x, center_y = 0, 0
tangential_stretch = 5
radial_stretch = 1.2
kwargs_lens = [
{'tangential_stretch': tangential_stretch, 'radial_stretch': radial_stretch, 'curvature': 1. / 10.5,
'direction': 0.,
'center_x': center_x, 'center_y': center_y}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag, tangential_stretch * radial_stretch, decimal=8)
center_x, center_y = 0, 0
tangential_stretch = 3
radial_stretch = -1
kwargs_lens = [
{'tangential_stretch': tangential_stretch, 'radial_stretch': radial_stretch, 'curvature': 1./10.5,
'direction': 0.,
'center_x': center_x, 'center_y': center_y}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
print(tangential_stretch, radial_stretch, 'stretches')
npt.assert_almost_equal(mag, tangential_stretch * radial_stretch, decimal=8)
center_x, center_y = 0, 0
tangential_stretch = -3
radial_stretch = -1
kwargs_lens = [
{'tangential_stretch': tangential_stretch, 'radial_stretch': radial_stretch, 'curvature': 1./10.5,
'direction': 0.,
'center_x': center_x, 'center_y': center_y}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag, tangential_stretch * radial_stretch, decimal=8)
center_x, center_y = 0, 0
tangential_stretch = 10.4
radial_stretch = 0.6
kwargs_lens = [
{'tangential_stretch': tangential_stretch, 'radial_stretch': radial_stretch, 'curvature': 1./10.5,
'direction': 0.,
'center_x': center_x, 'center_y': center_y}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag, tangential_stretch * radial_stretch, decimal=8)
def test_curved_arc_recovery(self):
"""
test whether the curved arc parameters are satisfied in differential form
"""
ext = LensModelExtensions(LensModel(lens_model_list=['CURVED_ARC_SIS_MST']))
center_x, center_y = 1, 1. # test works except at (0,0) where the direction angle is not well defined
tangential_stretch = 10.
radial_stretch = 1.2
curvature, direction = 0.02, 0.5
kwargs_lens = {'tangential_stretch': tangential_stretch, 'radial_stretch': radial_stretch,
'curvature': curvature, 'direction': direction, 'center_x': center_x, 'center_y': center_y}
self._test_curved_arc_recovery(kwargs_lens)
def _test_curved_arc_recovery(self, kwargs_arc_init):
ext = LensModelExtensions(LensModel(lens_model_list=['CURVED_ARC_SIS_MST']))
center_x, center_y = kwargs_arc_init['center_x'], kwargs_arc_init['center_y']
kwargs_arc = ext.curved_arc_estimate(center_x, center_y, [kwargs_arc_init])
lambda_rad, lambda_tan, orientation_angle, dlambda_tan_dtan, dlambda_tan_drad, dlambda_rad_drad, dlambda_rad_dtan, dphi_tan_dtan, dphi_tan_drad, dphi_rad_drad, dphi_rad_dtan = ext.radial_tangential_differentials(center_x, center_y, [kwargs_arc_init])
npt.assert_almost_equal(kwargs_arc['tangential_stretch'], kwargs_arc_init['tangential_stretch'], decimal=3)
npt.assert_almost_equal(kwargs_arc['radial_stretch'], kwargs_arc_init['radial_stretch'], decimal=3)
npt.assert_almost_equal(kwargs_arc['curvature'], kwargs_arc_init['curvature'], decimal=3)
npt.assert_almost_equal(dphi_tan_dtan, kwargs_arc_init['curvature'], decimal=3)
npt.assert_almost_equal(kwargs_arc['direction'], kwargs_arc_init['direction'], decimal=3)
npt.assert_almost_equal(dlambda_tan_dtan, 0, decimal=3)
class CoredDensityMST(LensProfileBase):
"""
approximate mass-sheet transform of a density core. This routine takes the parameters of the density core and
subtracts a mass=sheet that approximates the cored profile in it's center to counter-act (in approximation) this
model. This allows for better sampling of the mass-sheet transformed quantities that do not have strong covariances.
Attention!!! The interpretation of the result is that the mass sheet as 'CONVERGENCE' that is present needs to be
subtracted in post-processing.
"""
param_names = ['lambda_approx', 'r_core', 'center_x', 'center_y']
lower_limit_default = {
'lambda_approx': -1,
'r_core': 0,
'center_x': -100,
'center_y': -100
}
upper_limit_default = {
'lambda_approx': 10,
'r_core': 100,
'center_x': 100,
'center_y': 100
}
def __init__(self, profile_type='CORED_DENSITY'):
if profile_type == 'CORED_DENSITY':
self._profile = CoredDensity()
elif profile_type == 'CORED_DENSITY_2':
self._profile = CoredDensity2()
elif profile_type == 'CORED_DENSITY_EXP':
self._profile = CoredDensityExp()
else:
raise ValueError(
'profile_type %s not supported for CoredDensityMST instance.' %
profile_type)
self._convergence = Convergence()
super(CoredDensityMST, self).__init__()
def function(self, x, y, lambda_approx, r_core, center_x=0, center_y=0):
"""
lensing potential of approximate mass-sheet correction
:param x: x-coordinate
:param y: y-coordinate
:param lambda_approx: approximate mass sheet transform
:param r_core: core radius of the cored density profile
:param center_x: x-center of the profile
:param center_y: y-center of the profile
:return: lensing potential correction
"""
kappa_ext = 1 - lambda_approx
f_cored_density = self._profile.function(x, y, kappa_ext, r_core,
center_x, center_y)
f_ms = self._convergence.function(x, y, kappa_ext, center_x, center_y)
return f_cored_density - f_ms
def derivatives(self, x, y, lambda_approx, r_core, center_x=0, center_y=0):
"""
deflection angles of approximate mass-sheet correction
:param x: x-coordinate
:param y: y-coordinate
:param lambda_approx: approximate mass sheet transform
:param r_core: core radius of the cored density profile
:param center_x: x-center of the profile
:param center_y: y-center of the profile
:return: alpha_x, alpha_y
"""
kappa_ext = 1 - lambda_approx
f_x_cd, f_y_cd = self._profile.derivatives(x, y, kappa_ext, r_core,
center_x, center_y)
f_x_ms, f_y_ms = self._convergence.derivatives(x, y, kappa_ext,
center_x, center_y)
return f_x_cd - f_x_ms, f_y_cd - f_y_ms
def hessian(self, x, y, lambda_approx, r_core, center_x=0, center_y=0):
"""
Hessian terms of approximate mass-sheet correction
:param x: x-coordinate
:param y: y-coordinate
:param lambda_approx: approximate mass sheet transform
:param r_core: core radius of the cored density profile
:param center_x: x-center of the profile
:param center_y: y-center of the profile
:return: df/dxx, df/dyy, df/dxy
"""
kappa_ext = 1 - lambda_approx
f_xx_cd, f_yy_cd, f_xy_cd = self._profile.hessian(
x, y, kappa_ext, r_core, center_x, center_y)
f_xx_ms, f_yy_ms, f_xy_ms = self._convergence.hessian(
x, y, kappa_ext, center_x, center_y)
return f_xx_cd - f_xx_ms, f_yy_cd - f_yy_ms, f_xy_cd - f_xy_ms
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 TestCurvedArcSISMST(object):
"""
tests the source model routines
"""
def setup(self):
self.model = CurvedArcSISMST()
self.sis = SIS()
self.mst = Convergence()
def test_spp2stretch(self):
center_x, center_y = 1, 1
theta_E = 1
kappa = 0.1
center_x_spp, center_y_spp = 0., 0
tangential_stretch, radial_stretch, curvature, direction = self.model.sis_mst2stretch(
theta_E, kappa, center_x_spp, center_y_spp, center_x, center_y)
theta_E_new, kappa_new, center_x_spp_new, center_y_spp_new = self.model.stretch2sis_mst(
tangential_stretch, radial_stretch, curvature, direction, center_x,
center_y)
npt.assert_almost_equal(center_x_spp_new, center_x_spp, decimal=8)
npt.assert_almost_equal(center_y_spp_new, center_y_spp, decimal=8)
npt.assert_almost_equal(theta_E_new, theta_E, decimal=8)
npt.assert_almost_equal(kappa_new, kappa, decimal=8)
center_x, center_y = -1, 1
tangential_stretch, radial_stretch, curvature, direction = self.model.sis_mst2stretch(
theta_E, kappa, center_x_spp, center_y_spp, center_x, center_y)
theta_E_new, kappa_new, center_x_spp_new, center_y_spp_new = self.model.stretch2sis_mst(
tangential_stretch, radial_stretch, curvature, direction, center_x,
center_y)
npt.assert_almost_equal(center_x_spp_new, center_x_spp, decimal=8)
npt.assert_almost_equal(center_y_spp_new, center_y_spp, decimal=8)
npt.assert_almost_equal(theta_E_new, theta_E, decimal=8)
npt.assert_almost_equal(kappa_new, kappa, decimal=8)
center_x, center_y = 0, 0.5
tangential_stretch, radial_stretch, curvature, direction = self.model.sis_mst2stretch(
theta_E, kappa, center_x_spp, center_y_spp, center_x, center_y)
theta_E_new, kappa_new, center_x_spp_new, center_y_spp_new = self.model.stretch2sis_mst(
tangential_stretch, radial_stretch, curvature, direction, center_x,
center_y)
npt.assert_almost_equal(center_x_spp_new, center_x_spp, decimal=8)
npt.assert_almost_equal(center_y_spp_new, center_y_spp, decimal=8)
npt.assert_almost_equal(theta_E_new, theta_E, decimal=8)
npt.assert_almost_equal(kappa_new, kappa, decimal=8)
center_x, center_y = 0, -1.5
tangential_stretch, radial_stretch, r_curvature, direction = self.model.sis_mst2stretch(
theta_E, kappa, center_x_spp, center_y_spp, center_x, center_y)
print(tangential_stretch, radial_stretch, r_curvature, direction)
theta_E_new, kappa_new, center_x_spp_new, center_y_spp_new = self.model.stretch2sis_mst(
tangential_stretch, radial_stretch, r_curvature, direction,
center_x, center_y)
npt.assert_almost_equal(center_x_spp_new, center_x_spp, decimal=8)
npt.assert_almost_equal(center_y_spp_new, center_y_spp, decimal=8)
npt.assert_almost_equal(theta_E_new, theta_E, decimal=8)
npt.assert_almost_equal(kappa_new, kappa, decimal=8)
def test_function(self):
center_x, center_y = 0., 0.
x, y = 1, 1
radial_stretch = 1
output = self.model.function(x,
y,
tangential_stretch=2,
radial_stretch=radial_stretch,
curvature=1. / 2,
direction=0,
center_x=center_x,
center_y=center_y)
theta_E, kappa_ext, center_x_sis, center_y_sis = self.model.stretch2sis_mst(
tangential_stretch=2,
radial_stretch=radial_stretch,
curvature=1. / 2,
direction=0,
center_x=center_x,
center_y=center_y)
f_sis_out = self.sis.function(
1, 1, theta_E, center_x_sis, center_y_sis
) # - self.sis.function(0, 0, theta_E, center_x_sis, center_y_sis)
alpha_x, alpha_y = self.sis.derivatives(center_x, center_y, theta_E,
center_x_sis, center_y_sis)
f_sis_0_out = alpha_x * (x - center_x) + alpha_y * (y - center_y)
f_mst_out = self.mst.function(x,
y,
kappa_ext,
ra_0=center_x,
dec_0=center_y)
lambda_mst = 1. / radial_stretch
f_out = lambda_mst * (f_sis_out - f_sis_0_out) + f_mst_out
npt.assert_almost_equal(output, f_out, decimal=8)
def test_derivatives(self):
tangential_stretch = 5
radial_stretch = 1
curvature = 1. / 10
direction = 0.3
center_x = 0
center_y = 0
x, y = 1, 1
theta_E, kappa, center_x_spp, center_y_spp = self.model.stretch2sis_mst(
tangential_stretch, radial_stretch, curvature, direction, center_x,
center_y)
f_x_sis, f_y_sis = self.sis.derivatives(x, y, theta_E, center_x_spp,
center_y_spp)
f_x_mst, f_y_mst = self.mst.derivatives(x,
y,
kappa,
ra_0=center_x,
dec_0=center_y)
f_x0, f_y0 = self.sis.derivatives(center_x, center_y, theta_E,
center_x_spp, center_y_spp)
f_x_new, f_y_new = self.model.derivatives(x, y, tangential_stretch,
radial_stretch, curvature,
direction, center_x,
center_y)
npt.assert_almost_equal(f_x_new, f_x_sis + f_x_mst - f_x0, decimal=8)
npt.assert_almost_equal(f_y_new, f_y_sis + f_y_mst - f_y0, decimal=8)
def test_hessian(self):
lens = LensModel(lens_model_list=['CURVED_ARC_SIS_MST'])
center_x, center_y = 0, 0
tangential_stretch = 10
radial_stretch = 1
kwargs_lens = [{
'tangential_stretch': tangential_stretch,
'radial_stretch': radial_stretch,
'curvature': 1. / 10.5,
'direction': 0.,
'center_x': center_x,
'center_y': center_y
}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag,
tangential_stretch * radial_stretch,
decimal=8)
center_x, center_y = 2, 3
tangential_stretch = 10
radial_stretch = 1
kwargs_lens = [{
'tangential_stretch': tangential_stretch,
'radial_stretch': radial_stretch,
'curvature': 1. / 10.5,
'direction': 0.,
'center_x': center_x,
'center_y': center_y
}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag,
tangential_stretch * radial_stretch,
decimal=8)
center_x, center_y = 0, 0
tangential_stretch = 5
radial_stretch = 1.2
kwargs_lens = [{
'tangential_stretch': tangential_stretch,
'radial_stretch': radial_stretch,
'curvature': 1. / 10.5,
'direction': 0.,
'center_x': center_x,
'center_y': center_y
}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag,
tangential_stretch * radial_stretch,
decimal=8)
center_x, center_y = 0, 0
tangential_stretch = 3
radial_stretch = -1
kwargs_lens = [{
'tangential_stretch': tangential_stretch,
'radial_stretch': radial_stretch,
'curvature': 1. / 10.5,
'direction': 0.,
'center_x': center_x,
'center_y': center_y
}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
print(tangential_stretch, radial_stretch, 'stretches')
npt.assert_almost_equal(mag,
tangential_stretch * radial_stretch,
decimal=8)
center_x, center_y = 0, 0
tangential_stretch = -3
radial_stretch = -1
kwargs_lens = [{
'tangential_stretch': tangential_stretch,
'radial_stretch': radial_stretch,
'curvature': 1. / 10.5,
'direction': 0.,
'center_x': center_x,
'center_y': center_y
}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag,
tangential_stretch * radial_stretch,
decimal=8)
center_x, center_y = 0, 0
tangential_stretch = 10.4
radial_stretch = 0.6
kwargs_lens = [{
'tangential_stretch': tangential_stretch,
'radial_stretch': radial_stretch,
'curvature': 1. / 10.5,
'direction': 0.,
'center_x': center_x,
'center_y': center_y
}]
mag = lens.magnification(center_x, center_y, kwargs=kwargs_lens)
npt.assert_almost_equal(mag,
tangential_stretch * radial_stretch,
decimal=8)
class CurvedArcSISMST(LensProfileBase):
"""
lens model that describes a section of a highly magnified deflector region.
The parameterization is chosen to describe local observables efficient.
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', 'direction',
'center_x', 'center_y'
]
lower_limit_default = {
'tangential_stretch': -100,
'radial_stretch': -5,
'curvature': 0.000001,
'direction': -np.pi,
'center_x': -100,
'center_y': -100
}
upper_limit_default = {
'tangential_stretch': 100,
'radial_stretch': 5,
'curvature': 100,
'direction': np.pi,
'center_x': 100,
'center_y': 100
}
def __init__(self):
self._sis = SIS()
self._mst = Convergence()
super(CurvedArcSISMST, self).__init__()
@staticmethod
def stretch2sis_mst(tangential_stretch, radial_stretch, curvature,
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 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)
return theta_E, kappa_ext, center_x_sis, center_y_sis
@staticmethod
def sis_mst2stretch(theta_E, kappa_ext, center_x_sis, center_y_sis,
center_x, center_y):
"""
turn Singular power-law lens model into stretch parameterization at position (center_x, center_y)
This is the inverse function of stretch2spp()
:param theta_E: Einstein radius of SIS profile
:param kappa_ext: external convergence (MST factor 1 - kappa_ext)
:param center_x_sis: center of SPP model
:param center_y_sis: center of SPP model
:param center_x: center of curved model definition
:param center_y: center of curved model definition
:return: tangential_stretch, radial_stretch, curvature, direction
:return:
"""
r_curvature = np.sqrt((center_x_sis - center_x)**2 +
(center_y_sis - center_y)**2)
direction = np.arctan2(center_y - center_y_sis,
center_x - center_x_sis)
radial_stretch = 1. / (1 - kappa_ext)
tangential_stretch = 1 / (1 - (theta_E / r_curvature)) * radial_stretch
curvature = 1. / r_curvature
return tangential_stretch, radial_stretch, curvature, direction
def function(self, x, y, tangential_stretch, radial_stretch, curvature,
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 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, kappa_ext, center_x_sis, center_y_sis = self.stretch2sis_mst(
tangential_stretch, radial_stretch, curvature, direction, center_x,
center_y)
f_sis = self._sis.function(
x, y, theta_E, 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._sis.derivatives(center_x, center_y, theta_E,
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,
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 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, kappa_ext, center_x_sis, center_y_sis = self.stretch2sis_mst(
tangential_stretch, radial_stretch, curvature, direction, center_x,
center_y)
f_x_sis, f_y_sis = self._sis.derivatives(x, y, theta_E, center_x_sis,
center_y_sis)
f_x0, f_y0 = self._sis.derivatives(center_x, center_y, theta_E,
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,
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 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, kappa_ext, center_x_sis, center_y_sis = self.stretch2sis_mst(
tangential_stretch, radial_stretch, curvature, direction, center_x,
center_y)
f_xx_sis, f_yy_sis, f_xy_sis = self._sis.hessian(
x, y, theta_E, center_x_sis, center_y_sis)
f_xx_mst, f_yy_mst, f_xy_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_yy_sis + f_yy_mst, lambda_mst * f_xy_sis + f_xy_mst
class ShearReduced(LensProfileBase):
"""
reduced shear distortions :math:`\\gamma' = \\gamma / (1-\\kappa)`.
This distortion keeps the magnification as unity and, thus, does not change the size of apparent objects.
To keep the magnification at unity, it requires
.. math::
(1-\\kappa)^2 - \\gamma_1^2 - \\gamma_2^ = 1
Thus, for given pair of reduced shear :math:`(\\gamma'_1, \\gamma'_2)`, an additional convergence term is calculated
and added to the lensing distortions.
"""
param_names = ['gamma1', 'gamma2', 'ra_0', 'dec_0']
lower_limit_default = {
'gamma1': -0.5,
'gamma2': -0.5,
'ra_0': -100,
'dec_0': -100
}
upper_limit_default = {
'gamma1': 0.5,
'gamma2': 0.5,
'ra_0': 100,
'dec_0': 100
}
def __init__(self):
self._shear = Shear()
self._convergence = Convergence()
super(ShearReduced, self).__init__()
@staticmethod
def _kappa_reduced(gamma1, gamma2):
"""
compute convergence such that magnification is unity
:param gamma1: reduced shear
:param gamma2: reduced shear
:return: kappa
"""
kappa = 1 - 1. / np.sqrt(1 - gamma1**2 - gamma2**2)
gamma1_ = (1 - kappa) * gamma1
gamma2_ = (1 - kappa) * gamma2
return kappa, gamma1_, gamma2_
def function(self, x, y, gamma1, gamma2, ra_0=0, dec_0=0):
"""
:param x: x-coordinate (angle)
:param y: y0-coordinate (angle)
:param gamma1: shear component
:param gamma2: shear component
:param ra_0: x/ra position where shear deflection is 0
:param dec_0: y/dec position where shear deflection is 0
:return: lensing potential
"""
kappa, gamma1_, gamma2_ = self._kappa_reduced(gamma1, gamma2)
f_shear = self._shear.function(x, y, gamma1_, gamma2_, ra_0, dec_0)
f_kappa = self._convergence.function(x, y, kappa, ra_0, dec_0)
return f_shear + f_kappa
def derivatives(self, x, y, gamma1, gamma2, ra_0=0, dec_0=0):
"""
:param x: x-coordinate (angle)
:param y: y0-coordinate (angle)
:param gamma1: shear component
:param gamma2: shear component
:param ra_0: x/ra position where shear deflection is 0
:param dec_0: y/dec position where shear deflection is 0
:return: deflection angles
"""
kappa, gamma1_, gamma2_ = self._kappa_reduced(gamma1, gamma2)
f_x_shear, f_y_shear = self._shear.derivatives(x, y, gamma1_, gamma2_,
ra_0, dec_0)
f_x_kappa, f_y_kappa = self._convergence.derivatives(
x, y, kappa, ra_0, dec_0)
return f_x_shear + f_x_kappa, f_y_shear + f_y_kappa
def hessian(self, x, y, gamma1, gamma2, ra_0=0, dec_0=0):
"""
:param x: x-coordinate (angle)
:param y: y0-coordinate (angle)
:param gamma1: shear component
:param gamma2: shear component
:param ra_0: x/ra position where shear deflection is 0
:param dec_0: y/dec position where shear deflection is 0
:return: f_xx, f_xy, f_yx, f_yy
"""
kappa, gamma1_, gamma2_ = self._kappa_reduced(gamma1, gamma2)
f_xx_g, f_xy_g, f_yx_g, f_yy_g = self._shear.hessian(
x, y, gamma1_, gamma2_, ra_0, dec_0)
f_xx_k, f_xy_k, f_yx_k, f_yy_k = self._convergence.hessian(
x, y, kappa, ra_0, dec_0)
f_xx = f_xx_g + f_xx_k
f_yy = f_yy_g + f_yy_k
f_xy = f_xy_g + f_xy_k
return f_xx, f_xy, f_xy, f_yy