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)
def _generate_galaxy_catalog(cluster_m,
cluster_z,
cluster_c,
cosmo,
ngals,
zsrc,
Delta_SO=None,
massdef=None,
halo_profile_model=None,
zsrc_min=None,
zsrc_max=None,
shapenoise=None,
photoz_sigma_unscaled=None,
field_size=None):
"""A private function that skips the sanity checks on derived properties. This
function should only be used when called directly from `generate_galaxy_catalog`.
For a detailed description of each of the parameters, see the documentation of
`generate_galaxy_catalog`.
"""
# Set the source galaxy redshifts
galaxy_catalog = _draw_source_redshifts(zsrc, zsrc_min, zsrc_max, ngals)
# Add photo-z errors and pdfs to source galaxy redshifts
if photoz_sigma_unscaled is not None:
galaxy_catalog = _compute_photoz_pdfs(galaxy_catalog,
photoz_sigma_unscaled)
# Draw galaxy positions
galaxy_catalog = _draw_galaxy_positions(galaxy_catalog, ngals, cluster_z,
cosmo, field_size)
# Compute the shear on each source galaxy
gamt = compute_tangential_shear(galaxy_catalog['r_mpc'],
mdelta=cluster_m,
cdelta=cluster_c,
z_cluster=cluster_z,
z_source=galaxy_catalog['ztrue'],
cosmo=cosmo,
delta_mdef=Delta_SO,
halo_profile_model=halo_profile_model,
massdef=massdef,
z_src_model='single_plane')
gamx = np.zeros(ngals)
kappa = compute_convergence(galaxy_catalog['r_mpc'],
mdelta=cluster_m,
cdelta=cluster_c,
z_cluster=cluster_z,
z_source=galaxy_catalog['z'],
cosmo=cosmo,
delta_mdef=Delta_SO,
halo_profile_model=halo_profile_model,
massdef=massdef,
z_src_model='single_plane')
galaxy_catalog['gammat'] = gamt
galaxy_catalog['gammax'] = np.zeros(ngals)
galaxy_catalog['posangle'] = np.arctan2(galaxy_catalog['y_mpc'],
galaxy_catalog['x_mpc'])
#corresponding shear1,2 components
gam1 = -gamt * np.cos(2 * galaxy_catalog['posangle']) + gamx * np.sin(
2 * galaxy_catalog['posangle'])
gam2 = -gamt * np.sin(2 * galaxy_catalog['posangle']) - gamx * np.cos(
2 * galaxy_catalog['posangle'])
#instrinsic ellipticities
e1_intrinsic = 0
e2_intrinsic = 0
# Add shape noise to source galaxy shears
if shapenoise is not None:
e1_intrinsic = shapenoise * np.random.standard_normal(ngals)
e2_intrinsic = shapenoise * np.random.standard_normal(ngals)
# Compute ellipticities
galaxy_catalog['e1'], galaxy_catalog['e2'] = compute_lensed_ellipticity(
e1_intrinsic, e2_intrinsic, gam1, gam2, kappa)
if photoz_sigma_unscaled is not None:
return galaxy_catalog['ra', 'dec', 'e1', 'e2', 'z', 'ztrue', 'pzbins',
'pzpdf']
return galaxy_catalog['ra', 'dec', 'e1', 'e2', 'z', 'ztrue']