def _generate_galaxy_catalog(cluster_m,
cluster_z,
cluster_c,
cosmo,
ngals,
Delta_SO,
zsrc,
zsrc_min=0.4,
zsrc_max=7.,
shapenoise=None,
photoz_sigma_unscaled=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`.
Takes the same parameters and returns the same things as the before mentioned function.
For a more 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, cluster_z, 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, ngals)
# Draw galaxy positions
galaxy_catalog = _draw_galaxy_positions(galaxy_catalog, ngals, cluster_z,
cosmo)
# Compute the shear on each source galaxy
gamt = predict_reduced_tangential_shear(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='nfw',
z_src_model='single_plane')
galaxy_catalog['gammat'] = gamt
# Add shape noise to source galaxy shears
if shapenoise is not None:
galaxy_catalog['gammat'] += shapenoise * np.random.standard_normal(
ngals)
# Compute ellipticities
galaxy_catalog['posangle'] = np.arctan2(galaxy_catalog['y_mpc'],
galaxy_catalog['x_mpc'])
galaxy_catalog['e1'] = -galaxy_catalog['gammat'] * np.cos(
2 * galaxy_catalog['posangle'])
galaxy_catalog['e2'] = -galaxy_catalog['gammat'] * np.sin(
2 * galaxy_catalog['posangle'])
if photoz_sigma_unscaled is not None:
return galaxy_catalog['ra', 'dec', 'e1', 'e2', 'z', 'pzbins', 'pzpdf']
return galaxy_catalog['ra', 'dec', 'e1', 'e2', 'z']
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_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)