diff --git a/hierarc/LensPosterior/kin_constraints_composite.py b/hierarc/LensPosterior/kin_constraints_composite.py index 9d00ab4..f43b62b 100644 --- a/hierarc/LensPosterior/kin_constraints_composite.py +++ b/hierarc/LensPosterior/kin_constraints_composite.py @@ -7,6 +7,7 @@ from lenstronomy.Util import constants as const from lenstronomy.Analysis.light_profile import LightProfileAnalysis from lenstronomy.LightModel.light_model import LightModel +from lenstronomy.LensModel.Profiles.gnfw import GNFW class KinConstraintsComposite(KinConstraints): @@ -16,7 +17,7 @@ def __init__( z_source, gamma_in_array, log_m2l_array, - kappa_s_array, + alpha_Rs_array, r_s_angle_array, theta_E, theta_E_error, @@ -42,6 +43,7 @@ def __init__( num_kin_sampling=1000, multi_observations=False, rho0_array=None, + kappa_s_array=None, r_s_array=None, is_m2l_population_level=True, ): @@ -53,7 +55,7 @@ def __init__( :param log_m2l_array: array of log10(mass-to-light ratios) of the stellar component, needs to be in the unit/scaling such that m2l / sigma_crit * amp in the kwargs_lens_light provides the convergence amplitude of the stars - :param kappa_s_array: array of generalized NFW profile's convergence normalization at the scale radius + :param alpha_Rs_array: array of the deflection (angular units) at projected Rs :param r_s_angle_array: array of halo scale radii in arcsecond :param theta_E: Einstein radius (in arc seconds) :param theta_E_error: 1-sigma error on Einstein radius @@ -88,6 +90,7 @@ def __init__( :param multi_observations: bool, if True, interprets kwargs_aperture and kwargs_seeing as lists of multiple observations :param rho0_array: array of halo mass normalizations in M_sun / Mpc^3 + :param kappa_s_array: array of generalized NFW profile's convergence normalization at the scale radius :param r_s_array: array of halo scale radii in Mpc """ @@ -152,16 +155,23 @@ def __init__( log_m2l_scaling=log_m2l_scaling, ) - if self._check_arrays(kappa_s_array, r_s_angle_array): - self._kappa_s_array = kappa_s_array + if self._check_arrays(alpha_Rs_array, r_s_angle_array): + self._halo_normalization_array = alpha_Rs_array + self._is_normalization_alpha_Rs = True + self._r_scale_angle_array = r_s_angle_array + elif self._check_arrays(kappa_s_array, r_s_angle_array): + self._halo_normalization_array = kappa_s_array + self._is_normalization_alpha_Rs = False self._r_scale_angle_array = r_s_angle_array elif self._check_arrays(rho0_array, r_s_array): - self._kappa_s_array, self._r_scale_angle_array = self.get_kappa_s_r_s_angle( + kappa_s_array, self._r_scale_angle_array = self.get_kappa_s_r_s_angle( rho0_array, r_s_array ) + self._halo_normalization_array = kappa_s_array + self._is_normalization_alpha_Rs = False else: raise ValueError( - "Both kappa_s_array and r_s_angle_array, or rho0_array and r_s_array must be arrays of the same length!" + "Both alpha_Rs_array and r_s_angle_array, kappa_s_array and r_s_angle_array, or rho0_array and r_s_array must be arrays of the same length!" ) self.gamma_in_array = gamma_in_array @@ -172,10 +182,10 @@ def __init__( self._gamma_in_prior_std = gamma_in_prior_std if not is_m2l_population_level and not self._check_arrays( - self._kappa_s_array, log_m2l_array + self._halo_normalization_array, log_m2l_array ): raise ValueError( - "log_m2l_array must have the same length as rho0_array or kappa_s_array!" + "log_m2l_array must have the same length as alpha_Rs_array, kappa_s_array, or rho0_array!" ) @staticmethod @@ -210,22 +220,24 @@ def draw_lens(self, no_error=False): if no_error is True: if self._is_m2l_population_level: return ( - np.mean(self._kappa_s_array), + np.mean(self._halo_normalization_array), np.mean(self._r_scale_angle_array), self._r_eff, 1, ) else: return ( - np.mean(self._kappa_s_array), + np.mean(self._halo_normalization_array), np.mean(self._r_scale_angle_array), np.mean(self.log_m2l_array), self._r_eff, 1, ) - random_index = np.random.randint(low=0, high=len(self._kappa_s_array)) - kappa_s_draw = self._kappa_s_array[random_index] + random_index = np.random.randint( + low=0, high=len(self._halo_normalization_array) + ) + halo_normalization_draw = self._halo_normalization_array[random_index] r_scale_angle_draw = self._r_scale_angle_array[random_index] # we make sure no negative r_eff are being sampled @@ -235,11 +247,11 @@ def draw_lens(self, no_error=False): r_eff_draw = delta_r_eff * self._r_eff if self._is_m2l_population_level: - return kappa_s_draw, r_scale_angle_draw, r_eff_draw, delta_r_eff + return halo_normalization_draw, r_scale_angle_draw, r_eff_draw, delta_r_eff else: log_m2l_draw = self.log_m2l_array[random_index] return ( - kappa_s_draw, + halo_normalization_draw, r_scale_angle_draw, log_m2l_draw, r_eff_draw, @@ -290,8 +302,8 @@ def j_kin_draw_composite( the mean values instead :return: dimensionless kinematic component J() Birrer et al. 2016, 2019 """ - kappa_s_draw, r_scale_angle_draw, r_eff_draw, delta_r_eff = self.draw_lens( - no_error=no_error + halo_normalization_draw, r_scale_angle_draw, r_eff_draw, delta_r_eff = ( + self.draw_lens(no_error=no_error) ) kwargs_lens_stars = copy.deepcopy(self._kwargs_lens_light[0]) @@ -304,11 +316,20 @@ def j_kin_draw_composite( for kwargs in kwargs_light: kwargs["sigma"] *= delta_r_eff + # Input is alpha_Rs + if self._is_normalization_alpha_Rs: + alpha_Rs_draw = halo_normalization_draw + # Input is kappa_s + else: + alpha_Rs_draw = GNFW().kappa_s_to_alpha_Rs( + halo_normalization_draw, r_scale_angle_draw, gamma_in + ) + kwargs_lens = [ { "Rs": r_scale_angle_draw, "gamma_in": gamma_in, - "kappa_s": kappa_s_draw, + "alpha_Rs": alpha_Rs_draw, "center_x": 0, "center_y": 0, }, @@ -336,7 +357,7 @@ def j_kin_draw_composite_m2l(self, kwargs_anisotropy, gamma_in, no_error=False): :return: dimensionless kinematic component J() Birrer et al. 2016, 2019 """ ( - kappa_s_draw, + halo_normalization_draw, r_scale_angle_draw, log_m2l_draw, r_eff_draw, @@ -353,11 +374,20 @@ def j_kin_draw_composite_m2l(self, kwargs_anisotropy, gamma_in, no_error=False): for kwargs in kwargs_light: kwargs["sigma"] *= delta_r_eff + # Input is alpha_Rs + if self._is_normalization_alpha_Rs: + alpha_Rs_draw = halo_normalization_draw + # Input is kappa_s + else: + alpha_Rs_draw = GNFW().kappa_s_to_alpha_Rs( + halo_normalization_draw, r_scale_angle_draw, gamma_in + ) + kwargs_lens = [ { "Rs": r_scale_angle_draw, "gamma_in": gamma_in, - "kappa_s": kappa_s_draw, + "alpha_Rs": alpha_Rs_draw, "center_x": 0, "center_y": 0, }, diff --git a/test/test_LensPosterior/test_kin_constraints_composite.py b/test/test_LensPosterior/test_kin_constraints_composite.py index 5b7b779..ef39153 100644 --- a/test/test_LensPosterior/test_kin_constraints_composite.py +++ b/test/test_LensPosterior/test_kin_constraints_composite.py @@ -81,6 +81,7 @@ def test_likelihoodconfiguration_om(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -120,6 +121,7 @@ def test_likelihoodconfiguration_om(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -147,6 +149,7 @@ def test_likelihoodconfiguration_om(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=[], r_s_angle_array=[], theta_E=theta_E, @@ -237,6 +240,7 @@ def test_likelihoodconfiguration_gom(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -342,6 +346,7 @@ def test_likelihoodconfiguration_om(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -445,6 +450,7 @@ def test_likelihoodconfiguration_gom(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -474,6 +480,221 @@ def test_likelihoodconfiguration_gom(self): ) +class TestKinConstraintsCompositeAlphaRs(object): + def setup_method(self): + pass + + def test_likelihoodconfiguration_om(self): + anisotropy_model = "OM" + kwargs_aperture = { + "aperture_type": "shell", + "r_in": 0, + "r_out": 3 / 2.0, + "center_ra": 0.0, + "center_dec": 0, + } + kwargs_seeing = {"psf_type": "GAUSSIAN", "fwhm": 1.4} + + # numerical settings (not needed if power-law profiles with Hernquist light distribution is computed) + kwargs_numerics_galkin = { + "interpol_grid_num": 50, # numerical interpolation, should converge -> infinity + "log_integration": True, + # log or linear interpolation of surface brightness and mass models + "max_integrate": 100, + "min_integrate": 0.001, + } # lower/upper bound of numerical integrals + + # redshift + z_lens = 0.5 + z_source = 1.5 + + cosmo = FlatLambdaCDM(H0=70, Om0=0.3) + theta_E = 1.0 + r_eff = 1 + gamma = 2.1 + + kwargs_mge_light = { + "grid_spacing": 0.1, + "grid_num": 10, + "n_comp": 20, + "center_x": 0, + "center_y": 0, + } + + kwargs_kin_api_settings = { + "multi_observations": False, + "kwargs_numerics_galkin": kwargs_numerics_galkin, + "kwargs_mge_light": kwargs_mge_light, + "sampling_number": 50, + "num_kin_sampling": 50, + "num_psf_sampling": 50, + } + + kwargs_lens_light = [ + { + "R_sersic": 2, + "amp": 1, + "n_sersic": 2, + "center_x": 0, + "center_y": 0, + } + ] + lens_light_model_list = ["SERSIC"] + + gamma_in_array = np.linspace(0.1, 2.9, 5) + log_m2l_array = np.linspace(0.1, 1, 5) + + rho0_array = np.random.normal(5, 0, 100) + r_s_array = np.random.normal(0.1, 0, 100) + + # compute likelihood + kin_constraints = KinConstraintsComposite( + z_lens=z_lens, + z_source=z_source, + gamma_in_array=gamma_in_array, + log_m2l_array=log_m2l_array, + alpha_Rs_array=rho0_array, + kappa_s_array=[], + r_s_angle_array=r_s_array, + theta_E=theta_E, + theta_E_error=0.01, + gamma=gamma, + gamma_error=0.02, + r_eff=r_eff, + r_eff_error=0.05, + sigma_v_measured=[200], + sigma_v_error_independent=[10], + sigma_v_error_covariant=0, + kwargs_aperture=kwargs_aperture, + kwargs_seeing=kwargs_seeing, + anisotropy_model=anisotropy_model, + kwargs_lens_light=kwargs_lens_light, + lens_light_model_list=lens_light_model_list, + **kwargs_kin_api_settings + ) + + kin_constraints.draw_lens(no_error=True) + + kwargs_likelihood = kin_constraints.hierarchy_configuration(num_sample_model=5) + kwargs_likelihood["normalized"] = False + ln_class = LensLikelihood( + gamma_in_sampling=True, log_m2l_sampling=True, **kwargs_likelihood + ) + kwargs_kin = {"a_ani": 1} + ln_class.lens_log_likelihood( + cosmo, kwargs_lens={"gamma_in": 2, "log_m2l": 1}, kwargs_kin=kwargs_kin + ) + + +class TestKinConstraintsCompositeAlphaRsM2l(object): + def setup_method(self): + pass + + def test_likelihoodconfiguration_om(self): + anisotropy_model = "OM" + kwargs_aperture = { + "aperture_type": "shell", + "r_in": 0, + "r_out": 3 / 2.0, + "center_ra": 0.0, + "center_dec": 0, + } + kwargs_seeing = {"psf_type": "GAUSSIAN", "fwhm": 1.4} + + # numerical settings (not needed if power-law profiles with Hernquist light distribution is computed) + kwargs_numerics_galkin = { + "interpol_grid_num": 50, # numerical interpolation, should converge -> infinity + "log_integration": True, + # log or linear interpolation of surface brightness and mass models + "max_integrate": 100, + "min_integrate": 0.001, + } # lower/upper bound of numerical integrals + + # redshift + z_lens = 0.5 + z_source = 1.5 + + cosmo = FlatLambdaCDM(H0=70, Om0=0.3) + theta_E = 1.0 + r_eff = 1 + gamma = 2.1 + + kwargs_mge_light = { + "grid_spacing": 0.1, + "grid_num": 10, + "n_comp": 20, + "center_x": 0, + "center_y": 0, + } + + kwargs_kin_api_settings = { + "multi_observations": False, + "kwargs_numerics_galkin": kwargs_numerics_galkin, + "kwargs_mge_light": kwargs_mge_light, + "sampling_number": 50, + "num_kin_sampling": 50, + "num_psf_sampling": 50, + } + + kwargs_lens_light = [ + { + "R_sersic": 2, + "amp": 1, + "n_sersic": 2, + "center_x": 0, + "center_y": 0, + } + ] + lens_light_model_list = ["SERSIC"] + + gamma_in_array = np.linspace(0.1, 2.9, 5) + + log_m2l_array = np.random.uniform(0.1, 1, 5) + rho0_array = np.random.normal(5, 0, 5) + r_s_array = np.random.normal(0.1, 0, 5) + + # compute likelihood + kin_constraints = KinConstraintsComposite( + z_lens=z_lens, + z_source=z_source, + gamma_in_array=gamma_in_array, + log_m2l_array=log_m2l_array, + alpha_Rs_array=rho0_array, + kappa_s_array=[], + r_s_angle_array=r_s_array, + theta_E=theta_E, + theta_E_error=0.01, + gamma=gamma, + gamma_error=0.02, + r_eff=r_eff, + r_eff_error=0.05, + sigma_v_measured=[200], + sigma_v_error_independent=[10], + sigma_v_error_covariant=0, + kwargs_aperture=kwargs_aperture, + kwargs_seeing=kwargs_seeing, + anisotropy_model=anisotropy_model, + kwargs_lens_light=kwargs_lens_light, + lens_light_model_list=lens_light_model_list, + is_m2l_population_level=False, + gamma_in_prior_mean=2, + gamma_in_prior_std=0.5, + **kwargs_kin_api_settings + ) + + kin_constraints.draw_lens(no_error=True) + + kwargs_likelihood = kin_constraints.hierarchy_configuration(num_sample_model=5) + kwargs_likelihood["normalized"] = False + ln_class = LensLikelihood( + gamma_in_sampling=True, log_m2l_sampling=False, **kwargs_likelihood + ) + kwargs_kin = {"a_ani": 1} + ln_class.lens_log_likelihood( + cosmo, kwargs_lens={"gamma_in": 2, "log_m2l": 0}, kwargs_kin=kwargs_kin + ) + + class TestRaise(unittest.TestCase): def test_raise(self): with self.assertRaises(ValueError): @@ -532,6 +753,7 @@ def test_raise(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -609,6 +831,7 @@ def test_raise(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -683,6 +906,7 @@ def test_raise(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=[], r_s_angle_array=[], theta_E=theta_E, @@ -760,6 +984,7 @@ def test_raise_m2l(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -838,6 +1063,7 @@ def test_raise_m2l(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E, @@ -913,6 +1139,7 @@ def test_raise_m2l(self): z_source=z_source, gamma_in_array=gamma_in_array, log_m2l_array=log_m2l_array, + alpha_Rs_array=[], kappa_s_array=rho0_array, r_s_angle_array=r_s_array, theta_E=theta_E,