def setup(self):
from lenstronomy.LensModel.Profiles.nie import NIE
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
from lenstronomy.LensModel.Profiles.sis import SIS
self.nie = NIE()
self.spemd = SPEMD_SMOOTH()
self.sis = SIS()
class SPEMD(object):
"""
class for smooth power law ellipse mass density profile
"""
param_names = ['theta_E', 'gamma', 'e1', 'e2', 'center_x', 'center_y']
lower_limit_default = {'theta_E': 0, 'gamma': 0, 'e1': -0.5, 'e2': -0.5, 'center_x': -100, 'center_y': -100}
upper_limit_default = {'theta_E': 100, 'gamma': 100, 'e1': 0.5, 'e2': 0.5, 'center_x': 100, 'center_y': 100}
def __init__(self):
self.s2 = 0.00000001
self.spp = SPP()
self.spemd_smooth = SPEMD_SMOOTH()
def function(self, x, y, theta_E, gamma, e1, e2, center_x=0, center_y=0):
return self.spemd_smooth.function(x, y, theta_E, gamma, e1, e2, self.s2, center_x, center_y)
def derivatives(self, x, y, theta_E, gamma, e1, e2, center_x=0, center_y=0):
return self.spemd_smooth.derivatives(x, y, theta_E, gamma, e1, e2, self.s2, center_x, center_y)
def hessian(self, x, y, theta_E, gamma, e1, e2, center_x=0, center_y=0):
return self.spemd_smooth.hessian(x, y, theta_E, gamma, e1, e2, self.s2, center_x, center_y)
def mass_3d_lens(self, r, theta_E, gamma, e1, e2):
"""
computes the spherical power-law mass enclosed (with SPP routiune)
:param r:
:param theta_E:
:param gamma:
:param q:
:param phi_G:
:return:
"""
return self.spp.mass_3d_lens(r, theta_E, gamma)
def test_bounds(self):
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
profile = SPEMD_SMOOTH()
compute_bool = profile._parameter_constraints(q_fastell=-1,
gam=-1,
s2=-1,
q=-1)
assert compute_bool is False
class SPEMD(object):
"""
class for smooth power law ellipse mass density profile
"""
def __init__(self):
self.s2 = 0.00000001
self.spp = SPP()
self.spemd_smooth = SPEMD_SMOOTH()
def function(self, x, y, theta_E, gamma, q, phi_G, center_x=0, center_y=0):
return self.spemd_smooth.function(x, y, theta_E, gamma, q, phi_G, self.s2, center_x, center_y)
def derivatives(self, x, y, theta_E, gamma, q, phi_G, center_x=0, center_y=0):
return self.spemd_smooth.derivatives(x, y, theta_E, gamma, q, phi_G, self.s2, center_x, center_y)
def hessian(self, x, y, theta_E, gamma, q, phi_G, center_x=0, center_y=0):
return self.spemd_smooth.hessian(x, y, theta_E, gamma, q, phi_G, self.s2, center_x, center_y)
def mass_3d_lens(self, r, theta_E, gamma, q, phi_G):
"""
computes the spherical power-law mass enclosed (with SPP routiune)
:param r:
:param theta_E:
:param gamma:
:param q:
:param phi_G:
:return:
"""
return self.spp.mass_3d_lens(r, theta_E, gamma)
def convert_params(self, theta_E, gamma, q):
"""
:param theta_E: Einstein radius
:param gamma: power law slope
:param q: axis ratio
:return: prefactor to SPEMP profile for FASTELL
"""
return self.spemd_smooth.convert_params(theta_E, gamma, q)
def _import_class(self, lens_type, i, custom_class):
if lens_type == 'SHIFT':
from lenstronomy.LensModel.Profiles.alpha_shift import Shift
return Shift()
elif lens_type == 'SHEAR':
from lenstronomy.LensModel.Profiles.shear import Shear
return Shear()
elif lens_type == 'CONVERGENCE':
from lenstronomy.LensModel.Profiles.convergence import Convergence
return Convergence()
elif lens_type == 'FLEXION':
from lenstronomy.LensModel.Profiles.flexion import Flexion
return Flexion()
elif lens_type == 'POINT_MASS':
from lenstronomy.LensModel.Profiles.point_mass import PointMass
return PointMass()
elif lens_type == 'SIS':
from lenstronomy.LensModel.Profiles.sis import SIS
return SIS()
elif lens_type == 'SIS_TRUNCATED':
from lenstronomy.LensModel.Profiles.sis_truncate import SIS_truncate
return SIS_truncate()
elif lens_type == 'SIE':
from lenstronomy.LensModel.Profiles.sie import SIE
return SIE()
elif lens_type == 'SPP':
from lenstronomy.LensModel.Profiles.spp import SPP
return SPP()
elif lens_type == 'NIE':
from lenstronomy.LensModel.Profiles.nie import NIE
return NIE()
elif lens_type == 'NIE_SIMPLE':
from lenstronomy.LensModel.Profiles.nie import NIE_simple
return NIE_simple()
elif lens_type == 'CHAMELEON':
from lenstronomy.LensModel.Profiles.chameleon import Chameleon
return Chameleon()
elif lens_type == 'DOUBLE_CHAMELEON':
from lenstronomy.LensModel.Profiles.chameleon import DoubleChameleon
return DoubleChameleon()
elif lens_type == 'SPEP':
from lenstronomy.LensModel.Profiles.spep import SPEP
return SPEP()
elif lens_type == 'SPEMD':
from lenstronomy.LensModel.Profiles.spemd import SPEMD
return SPEMD()
elif lens_type == 'SPEMD_SMOOTH':
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
return SPEMD_SMOOTH()
elif lens_type == 'NFW':
from lenstronomy.LensModel.Profiles.nfw import NFW
return NFW()
elif lens_type == 'NFW_ELLIPSE':
from lenstronomy.LensModel.Profiles.nfw_ellipse import NFW_ELLIPSE
return NFW_ELLIPSE()
elif lens_type == 'TNFW':
from lenstronomy.LensModel.Profiles.tnfw import TNFW
return TNFW()
elif lens_type == 'CNFW':
from lenstronomy.LensModel.Profiles.cnfw import CNFW
return CNFW()
elif lens_type == 'SERSIC':
from lenstronomy.LensModel.Profiles.sersic import Sersic
return Sersic()
elif lens_type == 'SERSIC_ELLIPSE':
from lenstronomy.LensModel.Profiles.sersic_ellipse import SersicEllipse
return SersicEllipse()
elif lens_type == 'PJAFFE':
from lenstronomy.LensModel.Profiles.p_jaffe import PJaffe
return PJaffe()
elif lens_type == 'PJAFFE_ELLIPSE':
from lenstronomy.LensModel.Profiles.p_jaffe_ellipse import PJaffe_Ellipse
return PJaffe_Ellipse()
elif lens_type == 'HERNQUIST':
from lenstronomy.LensModel.Profiles.hernquist import Hernquist
return Hernquist()
elif lens_type == 'HERNQUIST_ELLIPSE':
from lenstronomy.LensModel.Profiles.hernquist_ellipse import Hernquist_Ellipse
return Hernquist_Ellipse()
elif lens_type == 'GAUSSIAN':
from lenstronomy.LensModel.Profiles.gaussian_potential import Gaussian
return Gaussian()
elif lens_type == 'GAUSSIAN_KAPPA':
from lenstronomy.LensModel.Profiles.gaussian_kappa import GaussianKappa
return GaussianKappa()
elif lens_type == 'GAUSSIAN_KAPPA_ELLIPSE':
from lenstronomy.LensModel.Profiles.gaussian_kappa_ellipse import GaussianKappaEllipse
return GaussianKappaEllipse()
elif lens_type == 'MULTI_GAUSSIAN_KAPPA':
from lenstronomy.LensModel.Profiles.multi_gaussian_kappa import MultiGaussianKappa
return MultiGaussianKappa()
elif lens_type == 'MULTI_GAUSSIAN_KAPPA_ELLIPSE':
from lenstronomy.LensModel.Profiles.multi_gaussian_kappa import MultiGaussianKappaEllipse
return MultiGaussianKappaEllipse()
elif lens_type == 'INTERPOL':
from lenstronomy.LensModel.Profiles.interpol import Interpol
return Interpol(grid=False, min_grid_number=100)
elif lens_type == 'INTERPOL_SCALED':
from lenstronomy.LensModel.Profiles.interpol import InterpolScaled
return InterpolScaled()
elif lens_type == 'SHAPELETS_POLAR':
from lenstronomy.LensModel.Profiles.shapelet_pot_polar import PolarShapelets
return PolarShapelets()
elif lens_type == 'SHAPELETS_CART':
from lenstronomy.LensModel.Profiles.shapelet_pot_cartesian import CartShapelets
return CartShapelets()
elif lens_type == 'DIPOLE':
from lenstronomy.LensModel.Profiles.dipole import Dipole
return Dipole()
elif lens_type == 'FOREGROUND_SHEAR':
from lenstronomy.LensModel.Profiles.shear import Shear
self._foreground_shear = True
self._foreground_shear_idex = i
return Shear()
elif lens_type == 'coreBURKERT':
from lenstronomy.LensModel.Profiles.coreBurkert import coreBurkert
return coreBurkert()
elif lens_type == 'NumericalAlpha':
from lenstronomy.LensModel.Profiles.numerical_deflections import NumericalAlpha
return NumericalAlpha(custom_class[i])
else:
raise ValueError('%s is not a valid lens model' % lens_type)
class TestNIE(object):
"""
tests the Gaussian methods
"""
def setup(self):
from lenstronomy.LensModel.Profiles.nie import NIE
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
from lenstronomy.LensModel.Profiles.sis import SIS
self.nie = NIE()
self.spemd = SPEMD_SMOOTH()
self.sis = SIS()
def test_function(self):
y = np.array([1., 2])
x = np.array([0., 0.])
theta_E = 1.
q = 0.9999
s = 0.00001
phi_G = 0
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
values = self.nie.function(x, y, theta_E, e1, e2, s_scale=s)
delta_pot = values[1] - values[0]
values_spemd = self.sis.function(x, y, theta_E)
delta_pot_spemd = values_spemd[1] - values_spemd[0]
npt.assert_almost_equal(delta_pot, delta_pot_spemd, decimal=4)
if bool_test is True:
q = 0.99
s = 0.000001
phi_G = 0
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
values = self.nie.function(x, y, theta_E, e1, e2, s_scale=s)
delta_pot = values[1] - values[0]
gamma = 2.
values_spemd = self.spemd.function(x,
y,
theta_E,
gamma,
e1,
e2,
s_scale=s)
delta_pot_spemd = values_spemd[1] - values_spemd[0]
npt.assert_almost_equal(delta_pot, delta_pot_spemd, decimal=2)
def test_derivatives(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.99999
phi_G = 0
s = 0.0000001
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_x, f_y = self.nie.derivatives(x, y, theta_E, e1, e2, s_scale=s)
f_x_spemd, f_y_spemd = self.sis.derivatives(x, y, theta_E)
npt.assert_almost_equal(f_x, f_x_spemd, decimal=4)
npt.assert_almost_equal(f_y, f_y_spemd, decimal=4)
if bool_test is True:
q = 0.99
s = 0.000001
phi_G = 0
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_x, f_y = self.nie.derivatives(x, y, theta_E, e1, e2, s_scale=s)
gamma = 2.
f_x_spemd, f_y_spemd = self.spemd.derivatives(x,
y,
theta_E,
gamma,
e1,
e2,
s_scale=s)
print(f_x / f_x_spemd, 'ratio deflections')
print(1 + (1 - q) / 2)
npt.assert_almost_equal(f_x, f_x_spemd, decimal=2)
npt.assert_almost_equal(f_y, f_y_spemd, decimal=2)
def test_hessian(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.999999
phi_G = 0
s = 0.0000001
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_xx, f_yy, f_xy = self.nie.hessian(x, y, theta_E, e1, e2, s_scale=s)
f_xx_spemd, f_yy_spemd, f_xy_spemd = self.sis.hessian(x, y, theta_E)
npt.assert_almost_equal(f_xx, f_xx_spemd, decimal=4)
npt.assert_almost_equal(f_yy, f_yy_spemd, decimal=4)
npt.assert_almost_equal(f_xy, f_xy_spemd, decimal=4)
def __init__(self):
self.s2 = 0.00000001
self.spp = SPP()
self.spemd_smooth = SPEMD_SMOOTH()
def test_bounds(self):
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
profile = SPEMD_SMOOTH()
theta_E, gamma, q, phi_G, s_scale = profile._parameter_constraints(
theta_E=-1, s_scale=0, gamma=3, q=2, phi_G=0)
assert theta_E == 0
class TestSPEMD(object):
"""
tests the Gaussian methods
"""
def setup(self):
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
self.SPEMD_SMOOT = SPEMD_SMOOTH()
from lenstronomy.LensModel.Profiles.nie import NIE
self.NIE = NIE()
def test_function(self):
phi_E = 1.
gamma = 2.
q = 0.999
phi_G = 1.
s_scale = 0.1
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
x = np.array([1., 2])
y = np.array([2, 0])
values = self.SPEMD_SMOOT.function(x, y, phi_E, gamma, e1, e2, s_scale)
if fastell4py_bool:
values_nie = self.NIE.function(x, y, phi_E, e1, e2, s_scale)
delta_f = values[0] - values[1]
delta_f_nie = values_nie[0] - values_nie[1]
npt.assert_almost_equal(delta_f, delta_f_nie, decimal=5)
else:
npt.assert_almost_equal(values, 0, decimal=5)
def test_derivatives(self):
x = np.array([1])
y = np.array([2])
phi_E = 1.
gamma = 2.
q = 0.7
phi_G = 1.
s_scale = 0.1
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_x, f_y = self.SPEMD_SMOOT.derivatives(x, y, phi_E, gamma, e1, e2,
s_scale)
if fastell4py_bool:
f_x_nie, f_y_nie = self.NIE.derivatives(x, y, phi_E, e1, e2,
s_scale)
#TODO test with higher precision, convention of theta_E might be slightly different from NIE with SPEMD
npt.assert_almost_equal(f_x, f_x_nie, decimal=2)
npt.assert_almost_equal(f_y, f_y_nie, decimal=2)
else:
npt.assert_almost_equal(f_x, 0, decimal=7)
npt.assert_almost_equal(f_y, 0, decimal=7)
def test_hessian(self):
x = np.array([1.])
y = np.array([2.])
phi_E = 1.
gamma = 2.
q = 0.9
phi_G = 1.
s_scale = 0.1
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_xx, f_yy, f_xy = self.SPEMD_SMOOT.hessian(x, y, phi_E, gamma, e1, e2,
s_scale)
if fastell4py_bool:
f_xx_nie, f_yy_nie, f_xy_nie = self.NIE.hessian(
x, y, phi_E, e1, e2, s_scale)
npt.assert_almost_equal(f_xx, f_xx_nie, decimal=3)
npt.assert_almost_equal(f_yy, f_yy_nie, decimal=3)
npt.assert_almost_equal(f_xy, f_xy_nie, decimal=3)
else:
npt.assert_almost_equal(f_xx, 0, decimal=7)
npt.assert_almost_equal(f_yy, 0, decimal=7)
npt.assert_almost_equal(f_xy, 0, decimal=7)
def test_bounds(self):
theta_E, gamma, q, phi_G, s_scale = self.SPEMD_SMOOT._parameter_constraints(
theta_E=-1, s_scale=0, gamma=3, q=2, phi_G=0)
assert theta_E == 0
def test_is_not_empty(self):
func = self.SPEMD_SMOOT.is_not_empty
assert func(0.1, 0.2)
assert func([0.1], [0.2])
assert func((0.1, 0.3), (0.2, 0.4))
assert func(np.array([0.1]), np.array([0.2]))
assert not func([], [])
assert not func(np.array([]), np.array([]))
class TestNIE(object):
"""
tests the Gaussian methods
"""
def setup(self):
self.nie = NIE()
self.spemd = SPEMD_SMOOTH()
self.sis = SIS()
def test_function(self):
y = np.array([1., 2])
x = np.array([0., 0.])
theta_E = 1.
q = 0.9999
s = 0.00001
phi_G = 0
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
values = self.nie.function(x, y, theta_E, e1, e2, s_scale=s)
delta_pot = values[1] - values[0]
values_spemd = self.sis.function(x, y, theta_E)
delta_pot_spemd = values_spemd[1] - values_spemd[0]
npt.assert_almost_equal(delta_pot, delta_pot_spemd, decimal=4)
if bool_test is True:
q = 0.99
s = 0.000001
phi_G = 0
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
values = self.nie.function(x, y, theta_E, e1, e2, s_scale=s)
delta_pot = values[1] - values[0]
gamma = 2.
values_spemd = self.spemd.function(x,
y,
theta_E,
gamma,
e1,
e2,
s_scale=s)
delta_pot_spemd = values_spemd[1] - values_spemd[0]
npt.assert_almost_equal(delta_pot, delta_pot_spemd, decimal=2)
def test_derivatives(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.99999
phi_G = 0
s = 0.0000001
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_x, f_y = self.nie.derivatives(x, y, theta_E, e1, e2, s_scale=s)
f_x_spemd, f_y_spemd = self.sis.derivatives(x, y, theta_E)
npt.assert_almost_equal(f_x, f_x_spemd, decimal=4)
npt.assert_almost_equal(f_y, f_y_spemd, decimal=4)
if bool_test is True:
q = 0.99
s = 0.000001
phi_G = 0
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_x, f_y = self.nie.derivatives(x, y, theta_E, e1, e2, s_scale=s)
gamma = 2.
f_x_spemd, f_y_spemd = self.spemd.derivatives(x,
y,
theta_E,
gamma,
e1,
e2,
s_scale=s)
print(f_x / f_x_spemd, 'ratio deflections')
print(1 + (1 - q) / 2)
npt.assert_almost_equal(f_x, f_x_spemd, decimal=2)
npt.assert_almost_equal(f_y, f_y_spemd, decimal=2)
def test_hessian(self):
x = np.array([1])
y = np.array([2])
theta_E = 1.
q = 0.999999
phi_G = 0
s = 0.0000001
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_xx, f_yy, f_xy = self.nie.hessian(x, y, theta_E, e1, e2, s_scale=s)
f_xx_spemd, f_yy_spemd, f_xy_spemd = self.sis.hessian(x, y, theta_E)
npt.assert_almost_equal(f_xx, f_xx_spemd, decimal=4)
npt.assert_almost_equal(f_yy, f_yy_spemd, decimal=4)
npt.assert_almost_equal(f_xy, f_xy_spemd, decimal=4)
def test_convergence2surface_brightness(self):
from lenstronomy.LightModel.Profiles.nie import NIE as NIE_Light
nie_light = NIE_Light()
kwargs = {'e1': 0.3, 'e2': -0.05, 's_scale': 0.5}
x, y = util.make_grid(numPix=10, deltapix=0.1)
f_xx, f_yy, f_xy = self.nie.hessian(x, y, theta_E=1, **kwargs)
kappa = 1 / 2. * (f_xx + f_yy)
flux = nie_light.function(x, y, amp=1, **kwargs)
npt.assert_almost_equal(kappa / np.sum(kappa),
flux / np.sum(flux),
decimal=5)
def test_static(self):
x, y = 1., 1.
phi_G, q = 0.3, 0.8
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
kwargs_lens = {'theta_E': 1., 's_scale': .1, 'e1': e1, 'e2': e2}
f_ = self.nie.function(x, y, **kwargs_lens)
self.nie.set_static(**kwargs_lens)
f_static = self.nie.function(x, y, **kwargs_lens)
npt.assert_almost_equal(f_, f_static, decimal=8)
self.nie.set_dynamic()
kwargs_lens = {'theta_E': 2., 's_scale': .1, 'e1': e1, 'e2': e2}
f_dyn = self.nie.function(x, y, **kwargs_lens)
assert f_dyn != f_static
class SPEMD(LensProfileBase):
"""
class for power law ellipse mass density profile.
This class effectively calls the class SPEMD_SMOOTH with a fixed and very small central smoothing scale
to perform the numerical integral using the FASTELL code by Renan Barkana.
The Einstein ring parameter converts to the definition used by GRAVLENS as follow:
(theta_E / theta_E_gravlens) = sqrt[ (1+q^2) / (2 q) ]
"""
param_names = ['theta_E', 'gamma', 'e1', 'e2', 'center_x', 'center_y']
lower_limit_default = {'theta_E': 0, 'gamma': 1.5, 'e1': -0.5, 'e2': -0.5, 'center_x': -100, 'center_y': -100}
upper_limit_default = {'theta_E': 100, 'gamma': 2.5, 'e1': 0.5, 'e2': 0.5, 'center_x': 100, 'center_y': 100}
def __init__(self):
self.s2 = 0.00000001 # smoothing scale as used to numerically compute a power-law profile
self.spp = SPP()
self.spemd_smooth = SPEMD_SMOOTH()
super(SPEMD, self).__init__()
def function(self, x, y, theta_E, gamma, e1, e2, center_x=0, center_y=0):
"""
:param x: x-coordinate (angle)
:param y: y-coordinate (angle)
:param theta_E: Einstein radius (angle), pay attention to specific definition!
:param gamma: logarithmic slope of the power-law profile. gamma=2 corresponds to isothermal
:param e1: eccentricity component
:param e2: eccentricity component
:param center_x: x-position of lens center
:param center_y: y-position of lens center
:return: lensing potential
"""
return self.spemd_smooth.function(x, y, theta_E, gamma, e1, e2, self.s2, center_x, center_y)
def derivatives(self, x, y, theta_E, gamma, e1, e2, center_x=0, center_y=0):
"""
:param x: x-coordinate (angle)
:param y: y-coordinate (angle)
:param theta_E: Einstein radius (angle), pay attention to specific definition!
:param gamma: logarithmic slope of the power-law profile. gamma=2 corresponds to isothermal
:param e1: eccentricity component
:param e2: eccentricity component
:param center_x: x-position of lens center
:param center_y: y-position of lens center
:return: deflection angles alpha_x, alpha_y
"""
return self.spemd_smooth.derivatives(x, y, theta_E, gamma, e1, e2, self.s2, center_x, center_y)
def hessian(self, x, y, theta_E, gamma, e1, e2, center_x=0, center_y=0):
"""
:param x: x-coordinate (angle)
:param y: y-coordinate (angle)
:param theta_E: Einstein radius (angle), pay attention to specific definition!
:param gamma: logarithmic slope of the power-law profile. gamma=2 corresponds to isothermal
:param e1: eccentricity component
:param e2: eccentricity component
:param center_x: x-position of lens center
:param center_y: y-position of lens center
:return: Hessian components f_xx, f_yy, f_xy
"""
return self.spemd_smooth.hessian(x, y, theta_E, gamma, e1, e2, self.s2, center_x, center_y)
def mass_3d_lens(self, r, theta_E, gamma, e1=None, e2=None):
"""
computes the spherical power-law mass enclosed (with SPP routine)
:param r: radius within the mass is computed
:param theta_E: Einstein radius
:param gamma: power-law slope
:param e1: eccentricity component (not used)
:param e2: eccentricity component (not used)
:return: mass enclosed a 3D radius r
"""
return self.spp.mass_3d_lens(r, theta_E, gamma)
def __init__(self):
self.s2 = 0.00000001 # smoothing scale as used to numerically compute a power-law profile
self.spp = SPP()
self.spemd_smooth = SPEMD_SMOOTH()
super(SPEMD, self).__init__()
def setup(self):
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
self.SPEMD_SMOOT = SPEMD_SMOOTH()
from lenstronomy.LensModel.Profiles.nie import NIE
self.NIE = NIE()
def __init__(self, lens_model_list, **kwargs):
"""
:param lens_model_list: list of strings with lens model names
:param foreground_shear: bool, when True, models a foreground non-linear shear distortion
"""
self.func_list = []
self._foreground_shear = False
for i, lens_type in enumerate(lens_model_list):
if lens_type == 'SHEAR':
from lenstronomy.LensModel.Profiles.external_shear import ExternalShear
self.func_list.append(ExternalShear())
elif lens_type == 'CONVERGENCE':
from lenstronomy.LensModel.Profiles.mass_sheet import MassSheet
self.func_list.append(MassSheet())
elif lens_type == 'FLEXION':
from lenstronomy.LensModel.Profiles.flexion import Flexion
self.func_list.append(Flexion())
elif lens_type == 'POINT_MASS':
from lenstronomy.LensModel.Profiles.point_mass import PointMass
self.func_list.append(PointMass())
elif lens_type == 'SIS':
from lenstronomy.LensModel.Profiles.sis import SIS
self.func_list.append(SIS())
elif lens_type == 'SIS_TRUNCATED':
from lenstronomy.LensModel.Profiles.sis_truncate import SIS_truncate
self.func_list.append(SIS_truncate())
elif lens_type == 'SIE':
from lenstronomy.LensModel.Profiles.sie import SIE
self.func_list.append(SIE())
elif lens_type == 'SPP':
from lenstronomy.LensModel.Profiles.spp import SPP
self.func_list.append(SPP())
elif lens_type == 'NIE':
from lenstronomy.LensModel.Profiles.nie import NIE
self.func_list.append(NIE())
elif lens_type == 'NIE_SIMPLE':
from lenstronomy.LensModel.Profiles.nie import NIE_simple
self.func_list.append(NIE_simple())
elif lens_type == 'CHAMELEON':
from lenstronomy.LensModel.Profiles.chameleon import Chameleon
self.func_list.append(Chameleon())
elif lens_type == 'DOUBLE_CHAMELEON':
from lenstronomy.LensModel.Profiles.chameleon import DoubleChameleon
self.func_list.append(DoubleChameleon())
elif lens_type == 'SPEP':
from lenstronomy.LensModel.Profiles.spep import SPEP
self.func_list.append(SPEP())
elif lens_type == 'SPEMD':
from lenstronomy.LensModel.Profiles.spemd import SPEMD
self.func_list.append(SPEMD())
elif lens_type == 'SPEMD_SMOOTH':
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
self.func_list.append(SPEMD_SMOOTH())
elif lens_type == 'NFW':
from lenstronomy.LensModel.Profiles.nfw import NFW
self.func_list.append(NFW(**kwargs))
elif lens_type == 'NFW_ELLIPSE':
from lenstronomy.LensModel.Profiles.nfw_ellipse import NFW_ELLIPSE
self.func_list.append(
NFW_ELLIPSE(interpol=False,
num_interp_X=1000,
max_interp_X=100))
elif lens_type == 'TNFW':
from lenstronomy.LensModel.Profiles.tnfw import TNFW
self.func_list.append(TNFW())
elif lens_type == 'SERSIC':
from lenstronomy.LensModel.Profiles.sersic import Sersic
self.func_list.append(Sersic())
elif lens_type == 'SERSIC_ELLIPSE':
from lenstronomy.LensModel.Profiles.sersic_ellipse import SersicEllipse
self.func_list.append(SersicEllipse())
elif lens_type == 'PJAFFE':
from lenstronomy.LensModel.Profiles.p_jaffe import PJaffe
self.func_list.append(PJaffe())
elif lens_type == 'PJAFFE_ELLIPSE':
from lenstronomy.LensModel.Profiles.p_jaffe_ellipse import PJaffe_Ellipse
self.func_list.append(PJaffe_Ellipse())
elif lens_type == 'HERNQUIST':
from lenstronomy.LensModel.Profiles.hernquist import Hernquist
self.func_list.append(Hernquist())
elif lens_type == 'HERNQUIST_ELLIPSE':
from lenstronomy.LensModel.Profiles.hernquist_ellipse import Hernquist_Ellipse
self.func_list.append(Hernquist_Ellipse())
elif lens_type == 'GAUSSIAN':
from lenstronomy.LensModel.Profiles.gaussian_potential import Gaussian
self.func_list.append(Gaussian())
elif lens_type == 'GAUSSIAN_KAPPA':
from lenstronomy.LensModel.Profiles.gaussian_kappa import GaussianKappa
self.func_list.append(GaussianKappa())
elif lens_type == 'GAUSSIAN_KAPPA_ELLIPSE':
from lenstronomy.LensModel.Profiles.gaussian_kappa_ellipse import GaussianKappaEllipse
self.func_list.append(GaussianKappaEllipse())
elif lens_type == 'MULTI_GAUSSIAN_KAPPA':
from lenstronomy.LensModel.Profiles.multi_gaussian_kappa import MultiGaussianKappa
self.func_list.append(MultiGaussianKappa())
elif lens_type == 'MULTI_GAUSSIAN_KAPPA_ELLIPSE':
from lenstronomy.LensModel.Profiles.multi_gaussian_kappa import MultiGaussianKappaEllipse
self.func_list.append(MultiGaussianKappaEllipse())
elif lens_type == 'INTERPOL':
from lenstronomy.LensModel.Profiles.interpol import Interpol_func
self.func_list.append(
Interpol_func(grid=False, min_grid_number=100))
elif lens_type == 'INTERPOL_SCALED':
from lenstronomy.LensModel.Profiles.interpol import Interpol_func_scaled
self.func_list.append(
Interpol_func_scaled(grid=False, min_grid_number=100))
elif lens_type == 'SHAPELETS_POLAR':
from lenstronomy.LensModel.Profiles.shapelet_pot_polar import PolarShapelets
self.func_list.append(PolarShapelets())
elif lens_type == 'SHAPELETS_CART':
from lenstronomy.LensModel.Profiles.shapelet_pot_cartesian import CartShapelets
self.func_list.append(CartShapelets())
elif lens_type == 'DIPOLE':
from lenstronomy.LensModel.Profiles.dipole import Dipole
self.func_list.append(Dipole())
elif lens_type == 'FOREGROUND_SHEAR':
from lenstronomy.LensModel.Profiles.external_shear import ExternalShear
self.func_list.append(ExternalShear())
self._foreground_shear = True
self._foreground_shear_idex = i
else:
raise ValueError('%s is not a valid lens model' % lens_type)
self._model_list = lens_model_list
def setup(self):
self.nie = NIE()
self.spemd = SPEMD_SMOOTH()
self.sis = SIS()
def _import_class(lens_type, custom_class, z_lens=None, z_source=None):
"""
:param lens_type: string, lens model type
:param custom_class: custom class
:param z_lens: lens redshift # currently only used in NFW_MC model as this is redshift dependent
:param z_source: source redshift # currently only used in NFW_MC model as this is redshift dependent
:return: class instance of the lens model type
"""
if lens_type == 'SHIFT':
from lenstronomy.LensModel.Profiles.alpha_shift import Shift
return Shift()
elif lens_type == 'SHEAR':
from lenstronomy.LensModel.Profiles.shear import Shear
return Shear()
elif lens_type == 'SHEAR_GAMMA_PSI':
from lenstronomy.LensModel.Profiles.shear import ShearGammaPsi
return ShearGammaPsi()
elif lens_type == 'CONVERGENCE':
from lenstronomy.LensModel.Profiles.convergence import Convergence
return Convergence()
elif lens_type == 'FLEXION':
from lenstronomy.LensModel.Profiles.flexion import Flexion
return Flexion()
elif lens_type == 'FLEXIONFG':
from lenstronomy.LensModel.Profiles.flexionfg import Flexionfg
return Flexionfg()
elif lens_type == 'POINT_MASS':
from lenstronomy.LensModel.Profiles.point_mass import PointMass
return PointMass()
elif lens_type == 'SIS':
from lenstronomy.LensModel.Profiles.sis import SIS
return SIS()
elif lens_type == 'SIS_TRUNCATED':
from lenstronomy.LensModel.Profiles.sis_truncate import SIS_truncate
return SIS_truncate()
elif lens_type == 'SIE':
from lenstronomy.LensModel.Profiles.sie import SIE
return SIE()
elif lens_type == 'SPP':
from lenstronomy.LensModel.Profiles.spp import SPP
return SPP()
elif lens_type == 'NIE':
from lenstronomy.LensModel.Profiles.nie import NIE
return NIE()
elif lens_type == 'NIE_SIMPLE':
from lenstronomy.LensModel.Profiles.nie import NIEMajorAxis
return NIEMajorAxis()
elif lens_type == 'CHAMELEON':
from lenstronomy.LensModel.Profiles.chameleon import Chameleon
return Chameleon()
elif lens_type == 'DOUBLE_CHAMELEON':
from lenstronomy.LensModel.Profiles.chameleon import DoubleChameleon
return DoubleChameleon()
elif lens_type == 'TRIPLE_CHAMELEON':
from lenstronomy.LensModel.Profiles.chameleon import TripleChameleon
return TripleChameleon()
elif lens_type == 'SPEP':
from lenstronomy.LensModel.Profiles.spep import SPEP
return SPEP()
elif lens_type == 'SPEMD':
from lenstronomy.LensModel.Profiles.spemd import SPEMD
return SPEMD()
elif lens_type == 'SPEMD_SMOOTH':
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
return SPEMD_SMOOTH()
elif lens_type == 'NFW':
from lenstronomy.LensModel.Profiles.nfw import NFW
return NFW()
elif lens_type == 'NFW_ELLIPSE':
from lenstronomy.LensModel.Profiles.nfw_ellipse import NFW_ELLIPSE
return NFW_ELLIPSE()
elif lens_type == 'NFW_ELLIPSE_GAUSS_DEC':
from lenstronomy.LensModel.Profiles.gauss_decomposition import NFWEllipseGaussDec
return NFWEllipseGaussDec()
elif lens_type == 'TNFW':
from lenstronomy.LensModel.Profiles.tnfw import TNFW
return TNFW()
elif lens_type == 'CNFW':
from lenstronomy.LensModel.Profiles.cnfw import CNFW
return CNFW()
elif lens_type == 'CNFW_ELLIPSE':
from lenstronomy.LensModel.Profiles.cnfw_ellipse import CNFW_ELLIPSE
return CNFW_ELLIPSE()
elif lens_type == 'CTNFW_GAUSS_DEC':
from lenstronomy.LensModel.Profiles.gauss_decomposition import CTNFWGaussDec
return CTNFWGaussDec()
elif lens_type == 'NFW_MC':
from lenstronomy.LensModel.Profiles.nfw_mass_concentration import NFWMC
return NFWMC(z_lens=z_lens, z_source=z_source)
elif lens_type == 'SERSIC':
from lenstronomy.LensModel.Profiles.sersic import Sersic
return Sersic()
elif lens_type == 'SERSIC_ELLIPSE_POTENTIAL':
from lenstronomy.LensModel.Profiles.sersic_ellipse_potential import SersicEllipse
return SersicEllipse()
elif lens_type == 'SERSIC_ELLIPSE_KAPPA':
from lenstronomy.LensModel.Profiles.sersic_ellipse_kappa import SersicEllipseKappa
return SersicEllipseKappa()
elif lens_type == 'SERSIC_ELLIPSE_GAUSS_DEC':
from lenstronomy.LensModel.Profiles.gauss_decomposition \
import SersicEllipseGaussDec
return SersicEllipseGaussDec()
elif lens_type == 'PJAFFE':
from lenstronomy.LensModel.Profiles.p_jaffe import PJaffe
return PJaffe()
elif lens_type == 'PJAFFE_ELLIPSE':
from lenstronomy.LensModel.Profiles.p_jaffe_ellipse import PJaffe_Ellipse
return PJaffe_Ellipse()
elif lens_type == 'HERNQUIST':
from lenstronomy.LensModel.Profiles.hernquist import Hernquist
return Hernquist()
elif lens_type == 'HERNQUIST_ELLIPSE':
from lenstronomy.LensModel.Profiles.hernquist_ellipse import Hernquist_Ellipse
return Hernquist_Ellipse()
elif lens_type == 'GAUSSIAN':
from lenstronomy.LensModel.Profiles.gaussian_potential import Gaussian
return Gaussian()
elif lens_type == 'GAUSSIAN_KAPPA':
from lenstronomy.LensModel.Profiles.gaussian_kappa import GaussianKappa
return GaussianKappa()
elif lens_type == 'GAUSSIAN_ELLIPSE_KAPPA':
from lenstronomy.LensModel.Profiles.gaussian_ellipse_kappa import GaussianEllipseKappa
return GaussianEllipseKappa()
elif lens_type == 'GAUSSIAN_ELLIPSE_POTENTIAL':
from lenstronomy.LensModel.Profiles.gaussian_ellipse_potential import GaussianEllipsePotential
return GaussianEllipsePotential()
elif lens_type == 'MULTI_GAUSSIAN_KAPPA':
from lenstronomy.LensModel.Profiles.multi_gaussian_kappa import MultiGaussianKappa
return MultiGaussianKappa()
elif lens_type == 'MULTI_GAUSSIAN_KAPPA_ELLIPSE':
from lenstronomy.LensModel.Profiles.multi_gaussian_kappa import MultiGaussianKappaEllipse
return MultiGaussianKappaEllipse()
elif lens_type == 'INTERPOL':
from lenstronomy.LensModel.Profiles.interpol import Interpol
return Interpol()
elif lens_type == 'INTERPOL_SCALED':
from lenstronomy.LensModel.Profiles.interpol import InterpolScaled
return InterpolScaled()
elif lens_type == 'SHAPELETS_POLAR':
from lenstronomy.LensModel.Profiles.shapelet_pot_polar import PolarShapelets
return PolarShapelets()
elif lens_type == 'SHAPELETS_CART':
from lenstronomy.LensModel.Profiles.shapelet_pot_cartesian import CartShapelets
return CartShapelets()
elif lens_type == 'DIPOLE':
from lenstronomy.LensModel.Profiles.dipole import Dipole
return Dipole()
elif lens_type == 'CURVED_ARC':
from lenstronomy.LensModel.Profiles.curved_arc import CurvedArc
return CurvedArc()
elif lens_type == 'ARC_PERT':
from lenstronomy.LensModel.Profiles.arc_perturbations import ArcPerturbations
return ArcPerturbations()
elif lens_type == 'coreBURKERT':
from lenstronomy.LensModel.Profiles.coreBurkert import CoreBurkert
return CoreBurkert()
elif lens_type == 'CORED_DENSITY':
from lenstronomy.LensModel.Profiles.cored_density import CoredDensity
return CoredDensity()
elif lens_type == 'CORED_DENSITY_2':
from lenstronomy.LensModel.Profiles.cored_density_2 import CoredDensity2
return CoredDensity2()
elif lens_type == 'CORED_DENSITY_MST':
from lenstronomy.LensModel.Profiles.cored_density_mst import CoredDensityMST
return CoredDensityMST(profile_type='CORED_DENSITY')
elif lens_type == 'CORED_DENSITY_2_MST':
from lenstronomy.LensModel.Profiles.cored_density_mst import CoredDensityMST
return CoredDensityMST(profile_type='CORED_DENSITY_2')
elif lens_type == 'NumericalAlpha':
from lenstronomy.LensModel.Profiles.numerical_deflections import NumericalAlpha
return NumericalAlpha(custom_class)
else:
raise ValueError('%s is not a valid lens model' % lens_type)
class TestSPEMD(object):
"""
tests the Gaussian methods
"""
def setup(self):
from lenstronomy.LensModel.Profiles.spemd_smooth import SPEMD_SMOOTH
self.SPEMD_SMOOT = SPEMD_SMOOTH()
from lenstronomy.LensModel.Profiles.nie import NIE
self.NIE = NIE()
def test_function(self):
phi_E = 1.
gamma = 2.
q = 0.999
phi_G = 1.
s_scale = 0.1
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
x = np.array([1., 2])
y = np.array([2, 0])
values = self.SPEMD_SMOOT.function(x, y, phi_E, gamma, e1, e2, s_scale)
if fastell4py_bool:
values_nie = self.NIE.function(x, y, phi_E, e1, e2, s_scale)
delta_f = values[0] - values[1]
delta_f_nie = values_nie[0] - values_nie[1]
npt.assert_almost_equal(delta_f, delta_f_nie, decimal=5)
else:
npt.assert_almost_equal(values, 0, decimal=5)
def test_derivatives(self):
x = np.array([1])
y = np.array([2])
phi_E = 1.
gamma = 2.
q = 1.
phi_G = 1.
s_scale = 0.1
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_x, f_y = self.SPEMD_SMOOT.derivatives(x, y, phi_E, gamma, e1, e2,
s_scale)
if fastell4py_bool:
f_x_nie, f_y_nie = self.NIE.derivatives(x, y, phi_E, e1, e2,
s_scale)
npt.assert_almost_equal(f_x, f_x_nie, decimal=4)
npt.assert_almost_equal(f_y, f_y_nie, decimal=4)
else:
npt.assert_almost_equal(f_x, 0, decimal=7)
npt.assert_almost_equal(f_y, 0, decimal=7)
q = 0.7
phi_G = 1.
s_scale = 0.001
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_x, f_y = self.SPEMD_SMOOT.derivatives(x, y, phi_E, gamma, e1, e2,
s_scale)
if fastell4py_bool:
f_x_nie, f_y_nie = self.NIE.derivatives(x, y, phi_E, e1, e2,
s_scale)
npt.assert_almost_equal(f_x, f_x_nie, decimal=4)
npt.assert_almost_equal(f_y, f_y_nie, decimal=4)
else:
npt.assert_almost_equal(f_x, 0, decimal=7)
npt.assert_almost_equal(f_y, 0, decimal=7)
def test_hessian(self):
x = np.array([1.])
y = np.array([2.])
phi_E = 1.
gamma = 2.
q = 0.9
phi_G = 1.
s_scale = 0.001
e1, e2 = param_util.phi_q2_ellipticity(phi_G, q)
f_xx, f_yy, f_xy = self.SPEMD_SMOOT.hessian(x, y, phi_E, gamma, e1, e2,
s_scale)
if fastell4py_bool:
f_xx_nie, f_yy_nie, f_xy_nie = self.NIE.hessian(
x, y, phi_E, e1, e2, s_scale)
npt.assert_almost_equal(f_xx, f_xx_nie, decimal=4)
npt.assert_almost_equal(f_yy, f_yy_nie, decimal=4)
npt.assert_almost_equal(f_xy, f_xy_nie, decimal=4)
else:
npt.assert_almost_equal(f_xx, 0, decimal=7)
npt.assert_almost_equal(f_yy, 0, decimal=7)
npt.assert_almost_equal(f_xy, 0, decimal=7)
def test_bounds(self):
compute_bool = self.SPEMD_SMOOT._parameter_constraints(q_fastell=-1,
gam=-1,
s2=-1,
q=-1)
assert compute_bool is False
def test_is_not_empty(self):
func = self.SPEMD_SMOOT.is_not_empty
assert func(0.1, 0.2)
assert func([0.1], [0.2])
assert func((0.1, 0.3), (0.2, 0.4))
assert func(np.array([0.1]), np.array([0.2]))
assert not func([], [])
assert not func(np.array([]), np.array([]))