Source code for lenstronomy.LightModel.Profiles.power_law

import lenstronomy.Util.param_util as param_util
from lenstronomy.LensModel.Profiles.spp import SPP
import numpy as np
import scipy.special as special

__all__ = ['PowerLaw']


[docs]class PowerLaw(object): """ class for power-law elliptical mass distribution """ param_names = ['amp', 'gamma', 'e1', 'e2', 'center_x', 'center_y'] lower_limit_default = {'amp': 0, 'gamma': 1, 'e1': -0.5, 'e2': -0.5, 'center_x': -100, 'center_y': -100} upper_limit_default = {'amp': 100, 'gamma': 3, 'e1': 0.5, 'e2': 0.5, 'center_x': 100, 'center_y': 100} def __init__(self): self.lens = SPP()
[docs] def function(self, x, y, amp, gamma, e1, e2, center_x=0, center_y=0): """ :param x: ra-coordinate :param y: dec-coordinate :param gamma: projected power-law slope :param e1: ellipticity :param e2: ellipticity :param center_x: center :param center_y: center :return: projected flux """ x_, y_ = param_util.transform_e1e2_product_average(x, y, e1, e2, center_x, center_y) P2 = x_ ** 2 + y_ ** 2 if isinstance(P2, int) or isinstance(P2, float): a = max(0.00000001, P2) else: a = np.empty_like(P2) p2 = P2[P2 > 0] # in the SIS regime a[P2 == 0] = 0.00000001 a[P2 > 0] = p2 sigma = amp * a ** ((1. - gamma)/2.) return sigma
[docs] def light_3d(self, r, amp, gamma, e1, e2): """ :param r: :param amp: :param gamma: :param e1: :param e2: :return: """ rho0 = self._amp2rho(amp, gamma) rho = rho0 / r ** gamma return rho
def _amp2rho(self, amp, gamma): """ :param amp: :param gamma: :return: """ factor = np.sqrt(np.pi) * special.gamma(1./2*(-1+gamma))/special.gamma(gamma/2.) return amp / factor