import numpy as np
from lenstronomy.LensModel.Profiles.gaussian_kappa import GaussianKappa
from lenstronomy.LensModel.Profiles.gaussian_ellipse_potential import GaussianEllipsePotential
from lenstronomy.LensModel.Profiles.base_profile import LensProfileBase
__all__ = ['MultiGaussianKappa', 'MultiGaussianKappaEllipse']
[docs]class MultiGaussianKappa(LensProfileBase):
"""
"""
param_names = ['amp', 'sigma', 'center_x', 'center_y']
lower_limit_default = {'amp': 0, 'sigma': 0, 'center_x': -100, 'center_y': -100}
upper_limit_default = {'amp': 100, 'sigma': 100, 'center_x': 100, 'center_y': 100}
def __init__(self):
self.gaussian_kappa = GaussianKappa()
super(MultiGaussianKappa, self).__init__()
[docs] def function(self, x, y, amp, sigma, center_x=0, center_y=0, scale_factor=1):
"""
:param x:
:param y:
:param amp:
:param sigma:
:param center_x:
:param center_y:
:return:
"""
f_ = np.zeros_like(x, dtype=float)
for i in range(len(amp)):
f_ += self.gaussian_kappa.function(x, y, amp=scale_factor*amp[i], sigma=sigma[i],
center_x=center_x, center_y=center_y)
return f_
[docs] def derivatives(self, x, y, amp, sigma, center_x=0, center_y=0, scale_factor=1):
"""
:param x:
:param y:
:param amp:
:param sigma:
:param center_x:
:param center_y:
:return:
"""
f_x, f_y = np.zeros_like(x, dtype=float), np.zeros_like(x, dtype=float)
for i in range(len(amp)):
f_x_i, f_y_i = self.gaussian_kappa.derivatives(x, y, amp=scale_factor*amp[i], sigma=sigma[i],
center_x=center_x, center_y=center_y)
f_x += f_x_i
f_y += f_y_i
return f_x, f_y
[docs] def hessian(self, x, y, amp, sigma, center_x=0, center_y=0, scale_factor=1):
"""
:param x:
:param y:
:param amp:
:param sigma:
:param center_x:
:param center_y:
:return:
"""
f_xx, f_yy, f_xy = np.zeros_like(x, dtype=float), np.zeros_like(x, dtype=float), np.zeros_like(x, dtype=float)
for i in range(len(amp)):
f_xx_i, f_xy_i, _, f_yy_i = self.gaussian_kappa.hessian(x, y, amp=scale_factor*amp[i],
sigma=sigma[i], center_x=center_x,
center_y=center_y)
f_xx += f_xx_i
f_yy += f_yy_i
f_xy += f_xy_i
return f_xx, f_xy, f_xy, f_yy
[docs] def density(self, r, amp, sigma, scale_factor=1):
"""
:param r:
:param amp:
:param sigma:
:return:
"""
d_ = np.zeros_like(r, dtype=float)
for i in range(len(amp)):
d_ += self.gaussian_kappa.density(r, scale_factor*amp[i], sigma[i])
return d_
[docs] def density_2d(self, x, y, amp, sigma, center_x=0, center_y=0, scale_factor=1):
"""
:param R:
:param am:
:param sigma_x:
:param sigma_y:
:return:
"""
d_3d = np.zeros_like(x, dtype=float)
for i in range(len(amp)):
d_3d += self.gaussian_kappa.density_2d(x, y, scale_factor*amp[i], sigma[i], center_x, center_y)
return d_3d
[docs] def mass_3d_lens(self, R, amp, sigma, scale_factor=1):
"""
:param R:
:param amp:
:param sigma:
:return:
"""
mass_3d = np.zeros_like(R, dtype=float)
for i in range(len(amp)):
mass_3d += self.gaussian_kappa.mass_3d_lens(R, scale_factor*amp[i], sigma[i])
return mass_3d
[docs]class MultiGaussianKappaEllipse(LensProfileBase):
"""
"""
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}
def __init__(self):
self.gaussian_kappa = GaussianEllipsePotential()
super(MultiGaussianKappaEllipse, self).__init__()
[docs] def function(self, x, y, amp, sigma, e1, e2, center_x=0, center_y=0, scale_factor=1):
"""
:param x:
:param y:
:param amp:
:param sigma:
:param center_x:
:param center_y:
:return:
"""
f_ = np.zeros_like(x, dtype=float)
for i in range(len(amp)):
f_ += self.gaussian_kappa.function(x, y, amp=scale_factor*amp[i], sigma=sigma[i], e1=e1, e2=e2,
center_x=center_x, center_y=center_y)
return f_
[docs] def derivatives(self, x, y, amp, sigma, e1, e2, center_x=0, center_y=0, scale_factor=1):
"""
:param x:
:param y:
:param amp:
:param sigma:
:param center_x:
:param center_y:
:return:
"""
f_x, f_y = np.zeros_like(x, dtype=float), np.zeros_like(x, dtype=float)
for i in range(len(amp)):
f_x_i, f_y_i = self.gaussian_kappa.derivatives(x, y, amp=scale_factor*amp[i], sigma=sigma[i], e1=e1, e2=e2,
center_x=center_x, center_y=center_y)
f_x += f_x_i
f_y += f_y_i
return f_x, f_y
[docs] def hessian(self, x, y, amp, sigma, e1, e2, center_x=0, center_y=0, scale_factor=1):
"""
:param x:
:param y:
:param amp:
:param sigma:
:param center_x:
:param center_y:
:return:
"""
f_xx, f_yy, f_xy = np.zeros_like(x, dtype=float), np.zeros_like(x, dtype=float), np.zeros_like(x, dtype=float)
for i in range(len(amp)):
f_xx_i, f_xy_i, _, f_yy_i = self.gaussian_kappa.hessian(x, y, amp=scale_factor*amp[i], sigma=sigma[i], e1=e1, e2=e2,
center_x=center_x, center_y=center_y)
f_xx += f_xx_i
f_yy += f_yy_i
f_xy += f_xy_i
return f_xx, f_xy, f_xy, f_yy
[docs] def density(self, r, amp, sigma, e1, e2, scale_factor=1):
"""
:param r:
:param amp:
:param sigma:
:return:
"""
d_ = np.zeros_like(r, dtype=float)
for i in range(len(amp)):
d_ += self.gaussian_kappa.density(r, scale_factor*amp[i], sigma[i], e1, e2)
return d_
[docs] def density_2d(self, x, y, amp, sigma, e1, e2, center_x=0, center_y=0, scale_factor=1):
"""
:param R:
:param am:
:param sigma_x:
:param sigma_y:
:return:
"""
d_3d = np.zeros_like(x, dtype=float)
for i in range(len(amp)):
d_3d += self.gaussian_kappa.density_2d(x, y, scale_factor*amp[i], sigma[i], e1, e2, center_x, center_y)
return d_3d
[docs] def mass_3d_lens(self, R, amp, sigma, e1, e2, scale_factor=1):
"""
:param R:
:param amp:
:param sigma:
:return:
"""
mass_3d = np.zeros_like(R, dtype=float)
for i in range(len(amp)):
mass_3d += self.gaussian_kappa.mass_3d_lens(R, scale_factor*amp[i], sigma[i], e1, e2)
return mass_3d