table_s['w_sys'] = 1
table_s['pix'] = healpy.ang2pix(nside,
table_s['ra'],
table_s['dec'],
lonlat=True)
all_pixs = np.unique(table_s['pix'])
use_pixs = np.zeros(0, dtype=np.int)
for pix in all_pixs:
near_pixs = healpy.pixelfunc.get_all_neighbours(nside, pix)
if np.all(np.isin(near_pixs, all_pixs)):
use_pixs = np.append(use_pixs, pix)
table_s = table_s[np.isin(table_s['pix'], use_pixs)]
table_s = add_maximum_lens_redshift(table_s, dz_min=0.15)
if 'd_com' not in table_s.colnames:
table_s['d_com'] = zebu.cosmo.comoving_transverse_distance(
table_s['z']).to(u.Mpc).value
if 'd_com_true' not in table_s.colnames:
table_s['d_com_true'] = zebu.cosmo.comoving_transverse_distance(
table_s['z_true']).to(u.Mpc).value
for lens_bin in range(n_lens_bins):
print('\t...lens bin {}'.format(lens_bin))
table_l = zebu.read_raw_data(stage,
'lens',
else:
table_s['e_1'] /= 1 + table_s['m']
table_s['e_2'] /= 1 + table_s['m']
table_s['m'] = 0
if survey == 'hsc':
table_s['e_1'] /= 2 * (1 - table_s['e_rms']**2)
table_s['e_2'] /= 2 * (1 - table_s['e_rms']**2)
table_s['e_rms'] = np.sqrt(0.5)
table_c['w_sys'] = 1.0
if args.zspec:
table_c['z'] = table_c['z_true']
table_s['z'] = table_s['z_true']
for table in [table_s, table_c]:
add_maximum_lens_redshift(table, dz_min=0.2, z_err_factor=0)
z_lens = np.linspace(zebu.lens_z_bins[lens_bin] - 1e-6,
zebu.lens_z_bins[lens_bin + 1] + 1e-6, 1000)
weight = np.zeros_like(z_lens)
for i in range(len(z_lens)):
sigma_crit = critical_surface_density(z_lens[i],
table_c['z'],
zebu.cosmo,
comoving=True)
weight[i] = np.sum(table_c['w'] * table_c['w_sys'] *
sigma_crit**-2 *
(z_lens[i] < table_c['z_l_max']))
fname.format(1)))[:, 0] + 0.025
table_n['n'] = np.vstack([
np.genfromtxt(os.path.join('kids', 'raw', fname.format(i + 1)))[:, 1]
for i in range(5)
]).T
z_s_bins = np.array([0.1, 0.3, 0.5, 0.7, 0.9, 1.2])
precompute_kwargs = {'table_n': table_n}
stacking_kwargs = {'scalar_shear_response_correction': True}
else:
raise ValueError("Survey must be 'des', 'hsc' or 'kids'.")
# %%
add_maximum_lens_redshift(table_s, dz_min=0.1)
if 'table_c' in precompute_kwargs.keys():
add_maximum_lens_redshift(table_c, dz_min=0.1)
if args.survey.lower() == 'kids':
table_s['z_l_max'] = 1000.0
precompute_kwargs.update({
'n_jobs': multiprocessing.cpu_count(),
'comoving': True,
'cosmology': cosmology
})
# %%
rp_bins = np.logspace(np.log10(0.5), np.log10(50.), 11)
table = dsigma_table(kv,
'source',
survey='KiDS',
version='KV450',
verbose=reg == 9)
for key in table.colnames:
table[key].unit = None
table = Table(table)
table_s = vstack([table_s, table])
table_s = table_s[table_s['z'] > 0.25 - 1e-6]
table_s = table_s[table_s['z'] < 1.2 + 1e-6]
table_s = add_maximum_lens_redshift(table_s, dz_min=0.105)
table_s['m'] = kids.multiplicative_shear_bias(table_s['z'],
version='KV450')
table_c = table_s[np.random.randint(len(table_s),
size=100000)]['z', 'z_l_max', 'w']
table_c['w_sys'] = np.ones(len(table_c))
table_c['z_true'] = np.zeros(len(table_c))
z_bins = [0.1, 0.3, 0.5, 0.7, 0.9, 1.2]
for i in range(len(z_bins) - 1):
nz = Table.read(os.path.join(
'kids', 'Nz_DIR_z{}t{}.asc'.format(z_bins[i], z_bins[i + 1])),
format='ascii',
def prepare_source_random(srcs,
rand,
calib=None,
photoz_cut='medium',
dz_min=0.1,
cosmology=None,
H0=70.0,
Om0=0.3,
comoving=False,
n_jobs=4,
r_min=0.15,
r_max=11,
n_bins=11,
output=None,
n_random=500000,
verbose=True):
"""Prepare the lensing source, calibration, and random catalogs.
Also precompute the DeltaSigma profiles for randoms if necessary, and define
the cosmology model and the radial bins used for lensing profiles.
"""
# Define cosmology
if cosmology is None:
cosmology = FlatLambdaCDM(H0=H0, Om0=Om0)
# Define radial bins
rp_bins = np.logspace(np.log10(r_min), np.log10(r_max), n_bins)
# Photo-z quality cuts
if verbose:
print("# Use the {:s} photo-z quality cut".format(photoz_cut))
if photoz_cut == 'basic':
photoz_mask = (srcs['frankenz_model_llmin'] < 6.)
elif photoz_cut == 'medium':
photoz_mask = (srcs['frankenz_model_llmin'] <
6.) & (srcs['frankenz_photoz_risk_best'] < 0.25)
elif photoz_cut == 'strict':
photoz_mask = (srcs['frankenz_model_llmin'] <
6.) & (srcs['frankenz_photoz_risk_best'] < 0.15)
else:
raise Exception(
"# Wrong photo-z quality cut type: [basic/medium/strict]")
# Prepare the source catalog
if verbose:
print("\n# Prepare the lensing source catalog")
srcs_use = helpers.dsigma_table(srcs[photoz_mask],
'source',
survey='hsc',
version='PDR2',
field='field',
z='frankenz_photoz_best',
z_low='frankenz_photoz_err68_min',
z_upp='frankenz_photoz_err68_max')
# Add maximum usable redshift for lenses
srcs_use = add_maximum_lens_redshift(srcs_use,
dz_min=dz_min,
z_err_factor=0,
apply_z_low=True)
# Prepare the calibration catalog if necessary
if calib is not None:
if verbose:
print("\n# Prepare the lensing calibration catalog")
# Photo-z quality cuts
if photoz_cut == 'basic':
photoz_mask = (calib['frankenz_model_llmin'] < 6.)
elif photoz_cut == 'medium':
photoz_mask = (calib['frankenz_model_llmin'] <
6.) & (calib['frankenz_photoz_risk_best'] < 0.25)
elif photoz_cut == 'strict':
photoz_mask = (calib['frankenz_model_llmin'] <
6.) & (calib['frankenz_photoz_risk_best'] < 0.15)
else:
raise Exception(
"# Wrong photo-z quality cut type: [basic/medium/strict]")
# Prepare the calibration catalog
calib_use = helpers.dsigma_table(calib[photoz_mask],
'calibration',
z='frankenz_photoz_best',
z_true='z_true',
z_low='frankenz_photoz_err68_min',
w='w_source',
w_sys='somw_cosmos_samplevaraince')
# Add maximum usable redshift for lenses
calib_use = add_maximum_lens_redshift(calib_use,
dz_min=dz_min,
z_err_factor=0,
apply_z_low=True)
else:
calib_use = None
# Prepare the random catalogs
if verbose:
print("\n# Prepare the random object catalog")
rand_use = prepare_random_catalog(rand, size=n_random)
# Pre-compute the DeltaSigma profiles for random objects
if verbose:
print("\n# Pre-compute the DeltaSigma profiles for random objects")
rand_pre = add_precompute_results(rand_use,
srcs_use,
rp_bins,
table_c=calib_use,
cosmology=cosmology,
comoving=comoving,
n_jobs=n_jobs)
# Remove the ones with no useful lensing information
rand_pre['n_s_tot'] = np.sum(rand_pre['sum 1'], axis=1)
rand_pre = rand_pre[rand_pre['n_s_tot'] > 0]
if output is not None:
srcs_use.write(output, path='source', format='hdf5')
calib_use.write(output, path='calib', format='hdf5', append=True)
rand_pre.write(output, path='random', format='hdf5', append=True)
return
else:
return {
'cosmology': cosmology,
'rp_bins': rp_bins,
'source': srcs_use,
'calib': calib_use,
'random': rand_pre
}