def test_get_critical_surface_density(modeling_data):
""" Validation test for critical surface density """
cfg = load_validation_config()
assert_allclose(
md.get_critical_surface_density(
cfg['cosmo'],
z_cluster=cfg['TEST_CASE']['z_cluster'],
z_source=cfg['TEST_CASE']['z_source']),
cfg['TEST_CASE']['nc_Sigmac'], 1.2e-8)
# Check behaviour when sources are in front of the lens
z_cluster = 0.3
z_source = 0.2
assert_allclose(
md.get_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(
md.get_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'],
1.2e-8)
def test_shear_convergence_unittests():
""" Unit and validation tests for the shear and convergence calculations """
helper_physics_functions(md.predict_tangential_shear)
helper_physics_functions(md.predict_convergence)
helper_physics_functions(md.predict_reduced_tangential_shear)
# 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 = md.get_critical_surface_density(cosmo,
cfg['GAMMA_PARAMS']['z_cluster'],
cfg['z_source'])
# Patch a conversion for cluster_toolkit computations
cosmo['Omega_c'] = cosmo['Omega_c'] * constants_conversion
cosmo['Omega_b'] = cosmo['Omega_b'] * constants_conversion
# Compute sigma_c in the new cosmology and get a correction factor
sigcrit_corr = cfg['SIGMAC_PHYSCONST_CORRECTION']
sigma_c_undo = md.get_critical_surface_density(
cosmo, cfg['GAMMA_PARAMS']['z_cluster'], +cfg['z_source'])
sigmac_corr = sigma_c_undo / sigma_c / sigcrit_corr
# Chech error is raised if too small radius
assert_raises(ValueError, md.predict_tangential_shear, 1.e-12, 1.e15, 4,
0.2, 0.45, cosmo)
# Validate tangential shear
gammat = md.predict_tangential_shear(cosmo=cosmo, **cfg['GAMMA_PARAMS'])
assert_allclose(gammat * sigmac_corr, cfg['numcosmo_profiles']['gammat'],
1.0e-8)
# Validate convergence
kappa = md.predict_convergence(cosmo=cosmo, **cfg['GAMMA_PARAMS'])
assert_allclose(kappa * sigmac_corr, cfg['numcosmo_profiles']['kappa'],
1.0e-8)
# Validate reduced tangential shear
assert_allclose(
md.predict_reduced_tangential_shear(cosmo=cosmo,
**cfg['GAMMA_PARAMS']),
gammat / (1.0 - kappa), 1.0e-10)
assert_allclose(gammat / (1. / sigmac_corr - kappa),
cfg['numcosmo_profiles']['gt'], 1.0e-6)
def test_get_critical_surface_density():
""" Validation test for critical surface density """
cfg = load_validation_config()
assert_allclose(cfg['SIGMAC_PHYSCONST_CORRECTION']*\
md.get_critical_surface_density(cfg['cosmo'],
z_cluster=cfg['TEST_CASE']['z_cluster'],
z_source=cfg['TEST_CASE']['z_source']),
cfg['TEST_CASE']['nc_Sigmac'], 1.0e-8)
def test_shear_convergence_unittests(modeling_data):
""" Unit and validation tests for the shear and convergence calculations """
helper_physics_functions(md.predict_tangential_shear)
helper_physics_functions(md.predict_convergence)
helper_physics_functions(md.predict_reduced_tangential_shear)
helper_physics_functions(md.predict_magnification)
# 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 = md.get_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 = md.get_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, md.predict_tangential_shear, 1.e-12, 1.e15, 4,
0.2, 0.45, cosmo)
# Validate tangential shear
gammat = md.predict_tangential_shear(cosmo=cosmo, **cfg['GAMMA_PARAMS'])
assert_allclose(gammat * sigmac_corr, cfg['numcosmo_profiles']['gammat'],
1.0e-8)
# Validate convergence
kappa = md.predict_convergence(cosmo=cosmo, **cfg['GAMMA_PARAMS'])
assert_allclose(kappa * sigmac_corr, cfg['numcosmo_profiles']['kappa'],
1.0e-8)
# Validate reduced tangential shear
assert_allclose(
md.predict_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'], 1.0e-6)
# Validate magnification
assert_allclose(
md.predict_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'], 4.0e-7)
# 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(
md.predict_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(
md.predict_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(
md.predict_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(
md.predict_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(
md.predict_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(
md.predict_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(
md.predict_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(
md.predict_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)