Source code for lenstronomy.LensModel.Profiles.multi_gaussian_kappa

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