__author__ = "sibirrer"
# this file contains a class to make a gaussian
import numpy as np
from lenstronomy.LensModel.Profiles.gaussian_kappa import GaussianKappa
import lenstronomy.Util.param_util as param_util
from lenstronomy.LensModel.Profiles.base_profile import LensProfileBase
__all__ = ["GaussianEllipsePotential"]
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class GaussianEllipsePotential(LensProfileBase):
"""This class contains functions to evaluate a Gaussian function and calculates its
derivative and hessian matrix with ellipticity in the convergence.
the calculation follows Glenn van de Ven et al. 2009
"""
param_names = ["amp", "sigma", "e1", "e2", "center_x", "center_y"]
lower_limit_default = {
"amp": 0,
"sigma": 0,
"e1": -0.5,
"e2": -0.5,
"center_x": -100,
"center_y": -100,
}
upper_limit_default = {
"amp": 100,
"sigma": 100,
"e1": 0.5,
"e2": 0.5,
"center_x": 100,
"center_y": 100,
}
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def __init__(self):
self.spherical = GaussianKappa()
self._diff = 0.000001
super(GaussianEllipsePotential, self).__init__()
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def function(self, x, y, amp, sigma, e1, e2, center_x=0, center_y=0):
"""Returns Gaussian."""
phi_G, q = param_util.ellipticity2phi_q(e1, e2)
x_shift = x - center_x
y_shift = y - center_y
cos_phi = np.cos(phi_G)
sin_phi = np.sin(phi_G)
e = abs(1 - q)
x_ = (cos_phi * x_shift + sin_phi * y_shift) * np.sqrt(1 - e)
y_ = (-sin_phi * x_shift + cos_phi * y_shift) * np.sqrt(1 + e)
f_ = self.spherical.function(x_, y_, amp=amp, sigma=sigma)
return f_
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def derivatives(self, x, y, amp, sigma, e1, e2, center_x=0, center_y=0):
"""Returns df/dx and df/dy of the function."""
phi_G, q = param_util.ellipticity2phi_q(e1, e2)
x_shift = x - center_x
y_shift = y - center_y
cos_phi = np.cos(phi_G)
sin_phi = np.sin(phi_G)
e = abs(1 - q)
x_ = (cos_phi * x_shift + sin_phi * y_shift) * np.sqrt(1 - e)
y_ = (-sin_phi * x_shift + cos_phi * y_shift) * np.sqrt(1 + e)
f_x_prim, f_y_prim = self.spherical.derivatives(x_, y_, amp=amp, sigma=sigma)
f_x_prim *= np.sqrt(1 - e)
f_y_prim *= np.sqrt(1 + e)
f_x = cos_phi * f_x_prim - sin_phi * f_y_prim
f_y = sin_phi * f_x_prim + cos_phi * f_y_prim
return f_x, f_y
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def hessian(self, x, y, amp, sigma, e1, e2, center_x=0, center_y=0):
"""Returns Hessian matrix of function d^2f/dx^2, d^2/dxdy, d^2/dydx,
d^f/dy^2."""
alpha_ra, alpha_dec = self.derivatives(
x, y, amp, sigma, e1, e2, center_x, center_y
)
diff = self._diff
alpha_ra_dx, alpha_dec_dx = self.derivatives(
x + diff, y, amp, sigma, e1, e2, center_x, center_y
)
alpha_ra_dy, alpha_dec_dy = self.derivatives(
x, y + diff, amp, sigma, e1, e2, center_x, center_y
)
f_xx = (alpha_ra_dx - alpha_ra) / diff
f_xy = (alpha_ra_dy - alpha_ra) / diff
f_yx = (alpha_dec_dx - alpha_dec) / diff
f_yy = (alpha_dec_dy - alpha_dec) / diff
return f_xx, f_xy, f_yx, f_yy
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def density(self, r, amp, sigma, e1, e2):
"""
:param r:
:param amp:
:param sigma:
:return:
"""
return self.spherical.density(r, amp, sigma)
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def density_2d(self, x, y, amp, sigma, e1, e2, center_x=0, center_y=0):
"""
:param x:
:param y:
:param amp:
:param sigma:
:param e1:
:param e2:
:param center_x:
:param center_y:
:return:
"""
return self.spherical.density_2d(x, y, amp, sigma, center_x, center_y)
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def mass_2d(self, R, amp, sigma, e1, e2):
"""
:param R:
:param amp:
:param sigma:
:param e1:
:param e2:
:return:
"""
return self.spherical.mass_2d(R, amp, sigma)
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def mass_3d(self, R, amp, sigma, e1, e2):
"""
:param R:
:param amp:
:param sigma:
:param e1:
:param e2:
:return:
"""
return self.spherical.mass_3d(R, amp, sigma)
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def mass_3d_lens(self, R, amp, sigma, e1, e2):
"""
:param R:
:param amp:
:param sigma:
:param e1:
:param e2:
:return:
"""
return self.spherical.mass_3d_lens(R, amp, sigma)
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def mass_2d_lens(self, R, amp, sigma, e1, e2):
"""
:param R:
:param amp:
:param sigma:
:param e1:
:param e2:
:return:
"""
return self.spherical.mass_2d_lens(R, amp, sigma)