def test_compare_power_law(self):
"""
compare power-law profiles analytical vs. numerical
:return:
"""
# light profile
light_profile_list = ['HERNQUIST']
r_eff = 1.5
kwargs_light = [{'Rs': 0.551 * r_eff, 'amp': 1.}] # effective half light radius (2d projected) in arcsec
# 0.551 *
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_profile = [{'theta_E': theta_E, 'gamma': gamma}] # Einstein radius (arcsec) and power-law slope
# anisotropy profile
anisotropy_type = 'OM'
r_ani = 2.
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
# aperture as slit
aperture_type = 'slit'
length = 1.
width = 0.3
kwargs_aperture = {'aperture_type': aperture_type, 'length': length, 'width': width, 'center_ra': 0, 'center_dec': 0, 'angle': 0}
psf_fwhm = 1. # Gaussian FWHM psf
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
kwargs_numerics = {'interpol_grid_num': 500, 'log_integration': True,
'max_integrate': 100}
kwargs_model = {'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list,
'anisotropy_model': anisotropy_type}
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
galkin = Galkin(kwargs_model=kwargs_model, kwargs_aperture=kwargs_aperture, kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo, kwargs_numerics=kwargs_numerics)
sigma_v = galkin.dispersion(kwargs_profile, kwargs_light, kwargs_anisotropy, sampling_number=1000)
kwargs_numerics = {'interpol_grid_num': 500, 'log_integration': False, 'max_integrate': 10}
galkin = Galkin(kwargs_model=kwargs_model, kwargs_aperture=kwargs_aperture, kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo, kwargs_numerics=kwargs_numerics, analytic_kinematics=False)
sigma_v_lin = galkin.dispersion(kwargs_profile, kwargs_light, kwargs_anisotropy, sampling_number=1000)
los_disp = Galkin(kwargs_model=kwargs_model, kwargs_aperture=kwargs_aperture, kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo, kwargs_numerics=kwargs_numerics, analytic_kinematics=True)
sigma_v2 = los_disp.dispersion(kwargs_mass={'gamma': gamma, 'theta_E': theta_E}, kwargs_light={'r_eff': r_eff},
kwargs_anisotropy={'r_ani':r_ani}, sampling_number=1000)
print(sigma_v, sigma_v_lin, sigma_v2, 'sigma_v Galkin (log and linear), sigma_v los dispersion')
npt.assert_almost_equal(sigma_v2/sigma_v, 1, decimal=2)
def velocity_dispersion_analytical(self,
theta_E,
gamma,
r_eff,
r_ani,
kappa_ext=0):
"""
computes the LOS velocity dispersion of the lens within a slit of size R_slit x dR_slit and seeing psf_fwhm.
The assumptions are a Hernquist light profile and the spherical power-law lens model at the first position and
an Osipkov and Merritt ('OM') stellar anisotropy distribution.
Further information can be found in the AnalyticKinematics() class.
:param theta_E: Einstein radius
:param gamma: power-low slope of the mass profile (=2 corresponds to isothermal)
:param r_ani: anisotropy radius in units of angles
:param r_eff: projected half-light radius
:param kappa_ext: external convergence not accounted in the lens models
:return: velocity dispersion in units [km/s]
"""
galkin = Galkin(kwargs_model={'anisotropy_model': 'OM'},
kwargs_aperture=self._kwargs_aperture_kin,
kwargs_psf=self._kwargs_psf_kin,
kwargs_cosmo=self._kwargs_cosmo,
kwargs_numerics={},
analytic_kinematics=True)
kwargs_profile = {'theta_E': theta_E, 'gamma': gamma}
kwargs_light = {'r_eff': r_eff}
kwargs_anisotropy = {'r_ani': r_ani}
sigma_v = galkin.dispersion(kwargs_profile,
kwargs_light,
kwargs_anisotropy,
sampling_number=self._sampling_number)
sigma_v = self.transform_kappa_ext(sigma_v, kappa_ext=kappa_ext)
return sigma_v
def test_log_vs_linear_integral(self):
# light profile
light_profile_list = ['HERNQUIST']
Rs = .5
kwargs_light = [{'Rs': Rs, 'amp': 1.}] # effective half light radius (2d projected) in arcsec
# 0.551 *
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_profile = [{'theta_E': theta_E, 'gamma': gamma}] # Einstein radius (arcsec) and power-law slope
# anisotropy profile
anisotropy_type = 'OM'
r_ani = 2.
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
# aperture as slit
aperture_type = 'slit'
length = 3.8
width = 0.9
kwargs_aperture = {'aperture_type': aperture_type, 'length': length, 'width': width, 'center_ra': 0, 'center_dec': 0, 'angle': 0}
psf_fwhm = 0.7 # Gaussian FWHM psf
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
kwargs_numerics_log = {'interpol_grid_num': 500, 'log_integration': True,
'max_integrate': 10}
kwargs_numerics_linear = {'interpol_grid_num': 500, 'log_integration': False,
'max_integrate': 10}
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
kwargs_model = {'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list,
'anisotropy_model': anisotropy_type}
galkin_linear = Galkin(kwargs_model=kwargs_model, kwargs_aperture=kwargs_aperture, kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo, kwargs_numerics=kwargs_numerics_linear)
sigma_v = galkin_linear.dispersion(kwargs_profile, kwargs_light, kwargs_anisotropy, sampling_number=1000)
galkin_log = Galkin(kwargs_model=kwargs_model, kwargs_aperture=kwargs_aperture, kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo, kwargs_numerics=kwargs_numerics_log)
sigma_v2 = galkin_log.dispersion(kwargs_profile, kwargs_light, kwargs_anisotropy, sampling_number=1000)
print(sigma_v, sigma_v2, 'sigma_v linear, sigma_v log')
print((sigma_v/sigma_v2)**2)
npt.assert_almost_equal(sigma_v/sigma_v2, 1, decimal=1)
def test_dispersion_map(self):
"""
tests whether the old and new version provide the same answer
"""
# light profile
light_profile_list = ['HERNQUIST']
r_eff = 1.5
kwargs_light = [{'Rs': r_eff, 'amp': 1.}] # effective half light radius (2d projected) in arcsec
# 0.551 *
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_mass = [{'theta_E': theta_E, 'gamma': gamma}] # Einstein radius (arcsec) and power-law slope
# anisotropy profile
anisotropy_type = 'OM'
r_ani = 2.
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
# aperture as shell
#aperture_type = 'shell'
#kwargs_aperture_inner = {'r_in': 0., 'r_out': 0.2, 'center_dec': 0, 'center_ra': 0}
#kwargs_aperture_outer = {'r_in': 0., 'r_out': 1.5, 'center_dec': 0, 'center_ra': 0}
# aperture as slit
r_bins = np.linspace(0, 2, 3)
kwargs_ifu = {'r_bins': r_bins, 'center_ra': 0, 'center_dec': 0, 'aperture_type': 'IFU_shells'}
kwargs_aperture = {'aperture_type': 'shell', 'r_in': r_bins[0], 'r_out': r_bins[1], 'center_ra': 0,
'center_dec': 0}
psf_fwhm = 1. # Gaussian FWHM psf
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
kwargs_numerics = {'interpol_grid_num': 500, 'log_integration': True,
'max_integrate': 100}
kwargs_model = {'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list,
'anisotropy_model': anisotropy_type}
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
galkinIFU = Galkin(kwargs_aperture=kwargs_ifu, kwargs_psf=kwargs_psf, kwargs_cosmo=kwargs_cosmo,
kwargs_model=kwargs_model, kwargs_numerics=kwargs_numerics, analytic_kinematics=True)
sigma_v_ifu = galkinIFU.dispersion_map(kwargs_mass={'theta_E': theta_E, 'gamma': gamma}, kwargs_light={'r_eff': r_eff},
kwargs_anisotropy=kwargs_anisotropy, num_kin_sampling=1000)
galkin = Galkin(kwargs_model, kwargs_aperture, kwargs_psf, kwargs_cosmo, kwargs_numerics,
analytic_kinematics=True)
sigma_v = galkin.dispersion(kwargs_mass={'theta_E': theta_E, 'gamma': gamma}, kwargs_light={'r_eff': r_eff},
kwargs_anisotropy=kwargs_anisotropy, sampling_number=1000)
npt.assert_almost_equal(sigma_v, sigma_v_ifu[0], decimal=-1)
def test_OMvsGOM(self):
"""
test OsivkopMerrit vs generalized OM model
:return:
"""
light_profile_list = ['HERNQUIST']
r_eff = 1.5
kwargs_light = [{
'Rs': r_eff,
'amp': 1.
}] # effective half light radius (2d projected) in arcsec
# 0.551 *
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_profile = [{
'theta_E': theta_E,
'gamma': gamma
}] # Einstein radius (arcsec) and power-law slope
# aperture as slit
aperture_type = 'slit'
length = 1.
width = 0.3
kwargs_aperture = {
'aperture_type': aperture_type,
'length': length,
'width': width,
'center_ra': 0,
'center_dec': 0,
'angle': 0
}
psf_fwhm = 1. # Gaussian FWHM psf
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
kwargs_numerics = {
'interpol_grid_num': 100,
'log_integration': True,
'max_integrate': 100,
'min_integrate': 0.001
}
# anisotropy profile
anisotropy_type = 'OM'
r_ani = 0.2
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
kwargs_model = {
'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list,
'anisotropy_model': anisotropy_type
}
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
galkin = Galkin(kwargs_model=kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_numerics=kwargs_numerics)
sigma_v_om = galkin.dispersion(kwargs_profile,
kwargs_light,
kwargs_anisotropy,
sampling_number=5000)
# anisotropy profile
anisotropy_type = 'GOM'
kwargs_anisotropy = {
'r_ani': r_ani,
'beta_inf': 1
} # anisotropy radius [arcsec]
kwargs_model = {
'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list,
'anisotropy_model': anisotropy_type
}
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
galkin_gom = Galkin(kwargs_model=kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_numerics=kwargs_numerics)
sigma_v_gom = galkin_gom.dispersion(kwargs_profile,
kwargs_light,
kwargs_anisotropy,
sampling_number=5000)
# warning: this tests does not work to this precision for every random seed. To increase precision, increase
# sampling_number
npt.assert_almost_equal(sigma_v_gom, sigma_v_om, decimal=0)
def test_2d_vs_3d_power_law(self):
# set up power-law light profile
light_model = ['POWER_LAW']
kwargs_light = [{'gamma': 2, 'amp': 1, 'e1': 0, 'e2': 0}]
lens_model = ['SIS']
kwargs_mass = [{'theta_E': 1}]
anisotropy_type = 'isotropic'
kwargs_anisotropy = {}
kwargs_model = {
'mass_profile_list': lens_model,
'light_profile_list': light_model,
'anisotropy_model': anisotropy_type
}
kwargs_numerics = {
'interpol_grid_num': 2000,
'log_integration': True,
'max_integrate': 50,
'min_integrate': 0.0001
}
kwargs_numerics_3d = copy.deepcopy(kwargs_numerics)
kwargs_numerics_3d['lum_weight_int_method'] = False
kwargs_numerics_2d = copy.deepcopy(kwargs_numerics)
kwargs_numerics_2d['lum_weight_int_method'] = True
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
# compute analytic velocity dispersion of SIS profile
v_sigma_c2 = kwargs_mass[0]['theta_E'] * const.arcsec / (
4 * np.pi) * kwargs_cosmo['d_s'] / kwargs_cosmo['d_ds']
v_sigma_true = np.sqrt(v_sigma_c2) * const.c / 1000
# aperture as slit
aperture_type = 'slit'
length = 1.
width = 0.3
kwargs_aperture = {
'aperture_type': aperture_type,
'length': length,
'width': width,
'center_ra': 0,
'center_dec': 0,
'angle': 0
}
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': 0.5}
galkin3d = Galkin(kwargs_model=kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_numerics=kwargs_numerics_3d)
galkin2d = Galkin(kwargs_model=kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_numerics=kwargs_numerics_2d)
sigma_draw_list = []
for i in range(100):
sigma_v_draw = galkin3d._draw_one_sigma2(kwargs_mass, kwargs_light,
kwargs_anisotropy)
sigma_draw_list.append(sigma_v_draw)
# print(np.sqrt(sigma_v_draw)/ 1000)
#import matplotlib.pyplot as plt
#plt.plot(np.sqrt(sigma_draw_list) / 1000 / v_sigma_true)
#plt.show()
print(np.sqrt(np.mean(sigma_draw_list)) / 1000, 'mean draw')
print('truth = ', v_sigma_true)
#assert 1 == 0
sigma_v_2d = galkin2d.dispersion(kwargs_mass,
kwargs_light,
kwargs_anisotropy,
sampling_number=1000)
sigma_v_3d = galkin3d.dispersion(kwargs_mass,
kwargs_light,
kwargs_anisotropy,
sampling_number=1000)
npt.assert_almost_equal(sigma_v_2d / v_sigma_true, 1, decimal=2)
npt.assert_almost_equal(sigma_v_3d / v_sigma_true, 1, decimal=2)
def test_projected_integral_vs_3d_rendering(self):
lum_weight_int_method = True
# light profile
light_profile_list = ['HERNQUIST']
r_eff = 1.5
kwargs_light = [{
'Rs': 0.551 * r_eff,
'amp': 1.
}] # effective half light radius (2d projected) in arcsec
# 0.551 *
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_profile = [{
'theta_E': theta_E,
'gamma': gamma
}] # Einstein radius (arcsec) and power-law slope
# anisotropy profile
anisotropy_type = 'OM'
r_ani = 2.
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
# aperture as slit
aperture_type = 'slit'
length = 1.
width = 0.3
kwargs_aperture = {
'aperture_type': aperture_type,
'length': length,
'width': width,
'center_ra': 0,
'center_dec': 0,
'angle': 0
}
psf_fwhm = 1. # Gaussian FWHM psf
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
kwargs_numerics_3d = {
'interpol_grid_num': 2000,
'log_integration': True,
'max_integrate': 1000,
'min_integrate': 0.00001,
'lum_weight_int_method': False
}
kwargs_model = {
'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list,
'anisotropy_model': anisotropy_type
}
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
galkin = Galkin(kwargs_model=kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_numerics=kwargs_numerics_3d)
sigma_v = galkin.dispersion(kwargs_profile,
kwargs_light,
kwargs_anisotropy,
sampling_number=1000)
kwargs_numerics_2d = {
'interpol_grid_num': 2000,
'log_integration': True,
'max_integrate': 1000,
'min_integrate': 0.00001,
'lum_weight_int_method': True
}
galkin = Galkin(kwargs_model=kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_numerics=kwargs_numerics_2d,
analytic_kinematics=False)
sigma_v_int_method = galkin.dispersion(kwargs_profile,
kwargs_light,
kwargs_anisotropy,
sampling_number=1000)
npt.assert_almost_equal(sigma_v_int_method / sigma_v, 1, decimal=2)
def test_log_vs_linear_integral(self):
"""
here we test logarithmic vs linear integral in an end-to-end fashion.
We do not demand the highest level of precisions here!!!
We are using the luminosity-weighted velocity dispersion integration calculation in this test.
"""
# light profile
light_profile_list = ['HERNQUIST']
Rs = .5
kwargs_light = [{
'Rs': Rs,
'amp': 1.
}] # effective half light radius (2d projected) in arcsec
# 0.551 *
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_profile = [{
'theta_E': theta_E,
'gamma': gamma
}] # Einstein radius (arcsec) and power-law slope
# anisotropy profile
anisotropy_type = 'OM'
r_ani = 2.
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
# aperture as slit
aperture_type = 'slit'
length = 3.8
width = 0.9
kwargs_aperture = {
'aperture_type': aperture_type,
'length': length,
'width': width,
'center_ra': 0,
'center_dec': 0,
'angle': 0
}
psf_fwhm = 0.7 # Gaussian FWHM psf
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
kwargs_numerics_log = {
'interpol_grid_num': 1000,
'log_integration': True,
'max_integrate': 10,
'min_integrate': 0.001,
'lum_weight_int_method': True
}
kwargs_numerics_linear = {
'interpol_grid_num': 1000,
'log_integration': False,
'max_integrate': 10,
'min_integrate': 0.001,
'lum_weight_int_method': True
}
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
kwargs_model = {
'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list,
'anisotropy_model': anisotropy_type
}
galkin_linear = Galkin(kwargs_model=kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_numerics=kwargs_numerics_linear)
sigma_v_lin = galkin_linear.dispersion(kwargs_profile,
kwargs_light,
kwargs_anisotropy,
sampling_number=1000)
galkin_log = Galkin(kwargs_model=kwargs_model,
kwargs_aperture=kwargs_aperture,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_numerics=kwargs_numerics_log)
sigma_v_log = galkin_log.dispersion(kwargs_profile,
kwargs_light,
kwargs_anisotropy,
sampling_number=1000)
print(sigma_v_lin, sigma_v_log, 'sigma_v linear, sigma_v log')
print((sigma_v_lin / sigma_v_log)**2)
npt.assert_almost_equal(sigma_v_lin / sigma_v_log, 1, decimal=2)
def test_sersic_vs_hernquist_kinematics(self):
"""
attention: this test only works for Sersic indices > \approx 2!
Lower n_sersic will result in different predictions with the Hernquist assumptions
replacing the correct Light model!
:return:
"""
# anisotropy profile
anisotropy_type = 'OM'
r_ani = 2.
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
# aperture as slit
aperture_type = 'slit'
length = 3.8
width = 0.9
kwargs_aperture = {
'length': length,
'width': width,
'center_ra': 0,
'center_dec': 0,
'angle': 0,
'aperture_type': aperture_type
}
psf_fwhm = 0.7 # Gaussian FWHM psf
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
# light profile
light_profile_list = ['SERSIC']
r_sersic = .3
n_sersic = 2.8
kwargs_light = [{
'amp': 1.,
'R_sersic': r_sersic,
'n_sersic': n_sersic,
'center_x': 0,
'center_y': 0
}] # effective half light radius (2d projected) in arcsec
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_profile = [{
'theta_E': theta_E,
'gamma': gamma
}] # Einstein radius (arcsec) and power-law slope
# Hernquist fit to Sersic profile
profile_analysis = LightProfileAnalysis(LightModel(['SERSIC']))
r_eff = profile_analysis.half_light_radius(kwargs_light,
grid_spacing=0.1,
grid_num=100)
print(r_eff)
light_profile_list_hernquist = ['HERNQUIST']
kwargs_light_hernquist = [{'Rs': r_eff * 0.551, 'amp': 1.}]
# mge of light profile
lightModel = LightModel(light_profile_list)
r_array = np.logspace(-3, 2, 100) * r_eff * 2
print(r_sersic / r_eff, 'r_sersic/r_eff')
flux_r = lightModel.surface_brightness(r_array, 0, kwargs_light)
amps, sigmas, norm = mge.mge_1d(r_array, flux_r, N=20)
light_profile_list_mge = ['MULTI_GAUSSIAN']
kwargs_light_mge = [{'amp': amps, 'sigma': sigmas}]
print(amps, sigmas, 'amp', 'sigma')
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
kwargs_model = {
'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list_hernquist,
'anisotropy_model': anisotropy_type
}
galkin = Galkin(kwargs_model=kwargs_model,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_aperture=kwargs_aperture,
kwargs_numerics={})
sigma_v = galkin.dispersion(kwargs_profile, kwargs_light_hernquist,
kwargs_anisotropy)
kwargs_model = {
'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list_mge,
'anisotropy_model': anisotropy_type
}
galkin = Galkin(kwargs_model=kwargs_model,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_aperture=kwargs_aperture,
kwargs_numerics={})
sigma_v2 = galkin.dispersion(kwargs_profile, kwargs_light_mge,
kwargs_anisotropy)
print(sigma_v, sigma_v2, 'sigma_v Galkin, sigma_v MGEn')
print((sigma_v / sigma_v2)**2)
npt.assert_almost_equal((sigma_v - sigma_v2) / sigma_v2, 0, decimal=1)
def test_mge_light_and_mass(self):
# anisotropy profile
anisotropy_model = 'OM'
r_ani = 2.
kwargs_anisotropy = {'r_ani': r_ani} # anisotropy radius [arcsec]
# aperture as slit
aperture_type = 'slit'
length = 3.8
width = 0.9
kwargs_aperture = {
'length': length,
'width': width,
'center_ra': 0,
'center_dec': 0,
'angle': 0,
'aperture_type': aperture_type
}
psf_fwhm = 0.7 # Gaussian FWHM psf
kwargs_cosmo = {'d_d': 1000, 'd_s': 1500, 'd_ds': 800}
# light profile
light_profile_list = ['HERNQUIST']
r_eff = 1.8
kwargs_light = [{
'Rs': r_eff,
'amp': 1.
}] # effective half light radius (2d projected) in arcsec
# mass profile
mass_profile_list = ['SPP']
theta_E = 1.2
gamma = 2.
kwargs_profile = [{
'theta_E': theta_E,
'gamma': gamma
}] # Einstein radius (arcsec) and power-law slope
# mge of light profile
lightModel = LightModel(light_profile_list)
r_array = np.logspace(-2, 2, 200) * r_eff * 2
flux_r = lightModel.surface_brightness(r_array, 0, kwargs_light)
amps, sigmas, norm = mge.mge_1d(r_array, flux_r, N=20)
light_profile_list_mge = ['MULTI_GAUSSIAN']
kwargs_light_mge = [{'amp': amps, 'sigma': sigmas}]
# mge of lens profile
lensModel = LensModel(mass_profile_list)
r_array = np.logspace(-2, 2, 200)
kappa_r = lensModel.kappa(r_array, 0, kwargs_profile)
amps, sigmas, norm = mge.mge_1d(r_array, kappa_r, N=20)
mass_profile_list_mge = ['MULTI_GAUSSIAN_KAPPA']
kwargs_profile_mge = [{'amp': amps, 'sigma': sigmas}]
kwargs_psf = {'psf_type': 'GAUSSIAN', 'fwhm': psf_fwhm}
kwargs_model = {
'mass_profile_list': mass_profile_list,
'light_profile_list': light_profile_list,
'anisotropy_model': anisotropy_model
}
kwargs_numerics = {
'interpol_grid_num': 100,
'log_integration': True,
'max_integrate': 100,
'min_integrate': 0.01
}
galkin = Galkin(kwargs_model=kwargs_model,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_aperture=kwargs_aperture,
kwargs_numerics=kwargs_numerics)
sigma_v = galkin.dispersion(kwargs_profile, kwargs_light,
kwargs_anisotropy)
kwargs_model_mge = {
'mass_profile_list': mass_profile_list_mge,
'light_profile_list': light_profile_list_mge,
'anisotropy_model': anisotropy_model
}
galkin = Galkin(kwargs_model=kwargs_model_mge,
kwargs_psf=kwargs_psf,
kwargs_cosmo=kwargs_cosmo,
kwargs_aperture=kwargs_aperture,
kwargs_numerics=kwargs_numerics)
sigma_v2 = galkin.dispersion(kwargs_profile_mge, kwargs_light_mge,
kwargs_anisotropy)
print(sigma_v, sigma_v2, 'sigma_v Galkin, sigma_v MGEn')
print((sigma_v / sigma_v2)**2)
npt.assert_almost_equal((sigma_v - sigma_v2) / sigma_v2, 0, decimal=2)
