import numpy as np
from lenstronomy.LensModel.Profiles.sis import SIS
from lenstronomy.LensModel.Profiles.convergence import Convergence
from lenstronomy.LensModel.Profiles.base_profile import LensProfileBase
__all__ = ["CurvedArcSISMST"]
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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,
}
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def __init__(self):
self._sis = SIS()
self._mst = Convergence()
super(CurvedArcSISMST, self).__init__()
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@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 = 1.0 / curvature
lambda_mst = 1.0 / radial_stretch
kappa_ext = 1 - lambda_mst
theta_E = r_curvature * (1.0 - radial_stretch / tangential_stretch)
return theta_E, kappa_ext, center_x_sis, center_y_sis
[docs]
@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 = 1.0 / (1 - kappa_ext)
tangential_stretch = 1 / (1 - (theta_E / r_curvature)) * radial_stretch
curvature = 1.0 / r_curvature
return tangential_stretch, radial_stretch, curvature, direction
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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 = 1.0 / 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
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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 = 1.0 / 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
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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 = 1.0 / 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_xy_sis, f_yx_sis, f_yy_sis = self._sis.hessian(
x, y, theta_E, 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,
)
def center_deflector(curvature, direction, center_x, center_y):
"""
: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: center_sis_x, center_sis_y
"""
center_x_sis = center_x - np.cos(direction) / curvature
center_y_sis = center_y - np.sin(direction) / curvature
return center_x_sis, center_y_sis