def test_compute_critical_surface_density(modeling_data):
""" Validation test for critical surface density """
reltol = modeling_data['theory_reltol']
cfg = load_validation_config()
assert_allclose(theo.compute_critical_surface_density(cfg['cosmo'],
z_cluster=cfg['TEST_CASE']['z_cluster'],
z_source=cfg['TEST_CASE']['z_source']),
cfg['TEST_CASE']['nc_Sigmac'], reltol)
# Check errors for z<0
assert_raises(ValueError, theo.compute_critical_surface_density, cfg['cosmo'], z_cluster=-0.2, z_source=0.3)
assert_raises(ValueError, theo.compute_critical_surface_density, cfg['cosmo'], z_cluster=0.2, z_source=-0.3)
# Check behaviour when sources are in front of the lens
z_cluster = 0.3
z_source = 0.2
assert_allclose(theo.compute_critical_surface_density(cfg['cosmo'], z_cluster=z_cluster, z_source=z_source),
np.inf, 1.0e-10)
z_source = [0.2, 0.12, 0.25]
assert_allclose(theo.compute_critical_surface_density(cfg['cosmo'], z_cluster=z_cluster, z_source=z_source),
[np.inf, np.inf, np.inf], 1.0e-10)
# Check usage with cluster object function
z_src = np.array([cfg['TEST_CASE']['z_source']])
cluster = GalaxyCluster(unique_id='blah', ra=0, dec=0, z=cfg['TEST_CASE']['z_cluster'],
galcat=GCData([0*z_src, 0*z_src, z_src],
names=('ra', 'dec', 'z')))
cluster.add_critical_surface_density(cfg['cosmo'])
assert_allclose(cluster.galcat['sigma_c'],
cfg['TEST_CASE']['nc_Sigmac'], reltol)
# Object Oriented tests
m = theo.Modeling()
m.set_cosmo(cfg['cosmo'])
assert_allclose(m.eval_critical_surface_density(cfg['TEST_CASE']['z_cluster'],
cfg['TEST_CASE']['z_source']),
cfg['TEST_CASE']['nc_Sigmac'], reltol)
# Check behaviour when sources are in front of the lens
z_cluster = 0.3
z_source = 0.2
assert_allclose(m.eval_critical_surface_density(z_cluster, z_source),
np.inf, 1.0e-10)
z_source = [0.2, 0.12, 0.25]
assert_allclose(m.eval_critical_surface_density(z_cluster, z_source),
[np.inf, np.inf, np.inf], 1.0e-10)
def test_shear_convergence_unittests(modeling_data):
""" Unit and validation tests for the shear and convergence calculations """
helper_physics_functions(theo.compute_tangential_shear)
helper_physics_functions(theo.compute_convergence)
helper_physics_functions(theo.compute_reduced_tangential_shear)
helper_physics_functions(theo.compute_magnification)
reltol = modeling_data['theory_reltol']
# Validation Tests -------------------------
# NumCosmo makes different choices for constants (Msun). We make this conversion
# by passing the ratio of SOLAR_MASS in kg from numcosmo and CLMM
cfg = load_validation_config()
constants_conversion = clc.SOLAR_MASS.value / cfg['TEST_CASE']['Msun[kg]']
# First compute SigmaCrit to correct cosmology changes
cosmo = cfg['cosmo']
sigma_c = theo.compute_critical_surface_density(
cosmo, cfg['GAMMA_PARAMS']['z_cluster'], cfg['z_source'])
# Compute sigma_c in the new cosmology and get a correction factor
sigma_c_undo = theo.compute_critical_surface_density(
cosmo, cfg['GAMMA_PARAMS']['z_cluster'], cfg['z_source'])
sigmac_corr = (sigma_c_undo / sigma_c)
# Chech error is raised if too small radius
assert_raises(ValueError, theo.compute_tangential_shear, 1.e-12, 1.e15, 4,
0.2, 0.45, cosmo)
# Validate tangential shear
gammat = theo.compute_tangential_shear(cosmo=cosmo, **cfg['GAMMA_PARAMS'])
assert_allclose(gammat * sigmac_corr, cfg['numcosmo_profiles']['gammat'],
reltol)
# Validate convergence
kappa = theo.compute_convergence(cosmo=cosmo, **cfg['GAMMA_PARAMS'])
assert_allclose(kappa * sigmac_corr, cfg['numcosmo_profiles']['kappa'],
reltol)
# Validate reduced tangential shear
assert_allclose(
theo.compute_reduced_tangential_shear(cosmo=cosmo,
**cfg['GAMMA_PARAMS']),
gammat / (1.0 - kappa), 1.0e-10)
assert_allclose(gammat * sigmac_corr / (1. - (kappa * sigmac_corr)),
cfg['numcosmo_profiles']['gt'], 1.e2 * reltol)
# Validate magnification
assert_allclose(
theo.compute_magnification(cosmo=cosmo, **cfg['GAMMA_PARAMS']),
1. / ((1 - kappa)**2 - abs(gammat)**2), 1.0e-10)
assert_allclose(1. / ((1 - kappa)**2 - abs(gammat)**2),
cfg['numcosmo_profiles']['mu'], 1.e2 * reltol)
# Check that shear, reduced shear and convergence return zero and magnification returns one if source is in front of the cluster
# First, check for a array of radius and single source z
r = np.logspace(-2, 2, 10)
z_cluster = 0.3
z_source = 0.2
assert_allclose(
theo.compute_convergence(r,
mdelta=1.e15,
cdelta=4.,
z_cluster=z_cluster,
z_source=z_source,
cosmo=cosmo), np.zeros(len(r)), 1.0e-10)
assert_allclose(
theo.compute_tangential_shear(r,
mdelta=1.e15,
cdelta=4.,
z_cluster=z_cluster,
z_source=z_source,
cosmo=cosmo), np.zeros(len(r)), 1.0e-10)
assert_allclose(
theo.compute_reduced_tangential_shear(r,
mdelta=1.e15,
cdelta=4.,
z_cluster=z_cluster,
z_source=z_source,
cosmo=cosmo), np.zeros(len(r)),
1.0e-10)
assert_allclose(
theo.compute_magnification(r,
mdelta=1.e15,
cdelta=4.,
z_cluster=z_cluster,
z_source=z_source,
cosmo=cosmo), np.ones(len(r)), 1.0e-10)
# Second, check a single radius and array of source z
r = 1.
z_source = [0.25, 0.1, 0.14, 0.02]
assert_allclose(
theo.compute_convergence(r,
mdelta=1.e15,
cdelta=4.,
z_cluster=z_cluster,
z_source=z_source,
cosmo=cosmo), np.zeros(len(z_source)),
1.0e-10)
assert_allclose(
theo.compute_tangential_shear(r,
mdelta=1.e15,
cdelta=4.,
z_cluster=z_cluster,
z_source=z_source,
cosmo=cosmo), np.zeros(len(z_source)),
1.0e-10)
assert_allclose(
theo.compute_reduced_tangential_shear(r,
mdelta=1.e15,
cdelta=4.,
z_cluster=z_cluster,
z_source=z_source,
cosmo=cosmo),
np.zeros(len(z_source)), 1.0e-10)
assert_allclose(
theo.compute_magnification(r,
mdelta=1.e15,
cdelta=4.,
z_cluster=z_cluster,
z_source=z_source,
cosmo=cosmo), np.ones(len(z_source)),
1.0e-10)
# Object Oriented tests
m = theo.Modeling()
m.set_cosmo(cosmo)
m.set_halo_density_profile(
halo_profile_model=cfg['GAMMA_PARAMS']['halo_profile_model'])
m.set_concentration(cfg['GAMMA_PARAMS']['cdelta'])
m.set_mass(cfg['GAMMA_PARAMS']['mdelta'])
# First compute SigmaCrit to correct cosmology changes
sigma_c = m.eval_critical_surface_density(cfg['GAMMA_PARAMS']['z_cluster'],
cfg['GAMMA_PARAMS']['z_source'])
# Compute sigma_c in the new cosmology and get a correction factor
sigma_c_undo = m.eval_critical_surface_density(
cfg['GAMMA_PARAMS']['z_cluster'], cfg['GAMMA_PARAMS']['z_source'])
sigmac_corr = (sigma_c_undo / sigma_c)
# Validate tangential shear
profile_pars = (cfg['GAMMA_PARAMS']['r_proj'],
cfg['GAMMA_PARAMS']['z_cluster'],
cfg['GAMMA_PARAMS']['z_source'])
gammat = m.eval_tangential_shear(*profile_pars)
assert_allclose(gammat * sigmac_corr, cfg['numcosmo_profiles']['gammat'],
reltol)
# Validate convergence
kappa = m.eval_convergence(*profile_pars)
assert_allclose(kappa * sigmac_corr, cfg['numcosmo_profiles']['kappa'],
reltol)
# Validate reduced tangential shear
assert_allclose(m.eval_reduced_tangential_shear(*profile_pars),
gammat / (1.0 - kappa), 1.0e-10)
assert_allclose(gammat * sigmac_corr / (1. - (kappa * sigmac_corr)),
cfg['numcosmo_profiles']['gt'], 1.e2 * reltol)
# Validate magnification
assert_allclose(m.eval_magnification(*profile_pars),
1. / ((1 - kappa)**2 - abs(gammat)**2), 1.0e-10)
assert_allclose(1. / ((1 - kappa)**2 - abs(gammat)**2),
cfg['numcosmo_profiles']['mu'], 1.e2 * reltol)
# Check that shear, reduced shear and convergence return zero and magnification returns one if source is in front of the cluster
# First, check for a array of radius and single source z
r = np.logspace(-2, 2, 10)
z_cluster = 0.3
z_source = 0.2
assert_allclose(m.eval_convergence(r, z_cluster, z_source),
np.zeros(len(r)), 1.0e-10)
assert_allclose(m.eval_tangential_shear(r, z_cluster, z_source),
np.zeros(len(r)), 1.0e-10)
assert_allclose(m.eval_reduced_tangential_shear(r, z_cluster, z_source),
np.zeros(len(r)), 1.0e-10)
assert_allclose(m.eval_magnification(r, z_cluster, z_source),
np.ones(len(r)), 1.0e-10)
# Second, check a single radius and array of source z
r = 1.
z_source = [0.25, 0.1, 0.14, 0.02]
assert_allclose(m.eval_convergence(r, z_cluster, z_source),
np.zeros(len(z_source)), 1.0e-10)
assert_allclose(m.eval_tangential_shear(r, z_cluster, z_source),
np.zeros(len(z_source)), 1.0e-10)
assert_allclose(m.eval_reduced_tangential_shear(r, z_cluster, z_source),
np.zeros(len(z_source)), 1.0e-10)
assert_allclose(m.eval_magnification(r, z_cluster, z_source),
np.ones(len(z_source)), 1.0e-10)