parser.add_argument('--region', type=int, help='region of the sky', default=1)
args = parser.parse_args()
# %%
output_directory = os.path.join('region_{}'.format(args.region), 'precompute')
if not os.path.exists(output_directory):
os.makedirs(output_directory)
try:
centers = np.genfromtxt(os.path.join(output_directory, 'centers.csv'))
except OSError:
table_l = zebu.read_mock_data('random', 0)
table_l = add_continous_fields(table_l, distance_threshold=1)
centers = jackknife_field_centers(table_l, n_jk=100)
np.savetxt(os.path.join(output_directory, 'centers.csv'), centers)
# %%
source_magnification = args.stage >= 2
lens_magnification = args.stage >= 3
fiber_assignment = args.stage >= 4
if args.stage == 0:
survey_list = ['gen']
else:
if args.source_bin < 4:
survey_list = ['des', 'hsc', 'kids']
else:
survey_list = ['kids']
'cosmology': cosmology
})
# Pre-compute the signal.
add_precompute_results(table_l, table_s, rp_bins, **precompute_kwargs)
add_precompute_results(table_r, table_s, rp_bins, **precompute_kwargs)
# Add jackknife fields.
table_l['n_s_tot'] = np.sum(table_l['sum 1'], axis=1)
table_l = table_l[table_l['n_s_tot'] > 0]
table_r['n_s_tot'] = np.sum(table_r['sum 1'], axis=1)
table_r = table_r[table_r['n_s_tot'] > 0]
add_continous_fields(table_l, distance_threshold=2)
centers = jackknife_field_centers(table_l, 100, weight='n_s_tot')
add_jackknife_fields(table_l, centers)
add_jackknife_fields(table_r, centers)
# Stack the signal.
stacking_kwargs['random_subtraction'] = True
for lens_bin in range(len(z_bins) - 1):
mask_l = ((z_bins[lens_bin] <= table_l['z']) &
(table_l['z'] < z_bins[lens_bin + 1]))
mask_r = ((z_bins[lens_bin] <= table_r['z']) &
(table_r['z'] < z_bins[lens_bin + 1]))
stacking_kwargs['table_r'] = table_r[mask_r]
stacking_kwargs['return_table'] = True
result = excess_surface_density(table_l[mask_l], **stacking_kwargs)
n_jobs=40,
comoving=False,
table_c=table_c,
cosmology=FlatLambdaCDM(H0=70, Om0=0.3))
print('Working on randoms in bin {}...'.format(lens_bin + 1))
table_r_pre = precompute_catalog(table_r,
table_s,
rp_bins,
n_jobs=40,
comoving=False,
table_c=table_c,
cosmology=FlatLambdaCDM(H0=70, Om0=0.3))
# Create the jackknife fields.
table_l_pre = add_continous_fields(table_l_pre, distance_threshold=2)
centers = jackknife_field_centers(table_l_pre, 100)
table_l_pre = add_jackknife_fields(table_l_pre, centers)
table_r_pre = add_jackknife_fields(table_r_pre, centers)
kwargs = {
'return_table': True,
'shear_bias_correction': True,
'random_subtraction': True,
'photo_z_dilution_correction': True,
'table_r': table_r_pre
}
result = excess_surface_density(table_l_pre, **kwargs)
kwargs['return_table'] = False
ds_cov = jackknife_resampling(excess_surface_density, table_l_pre,
**kwargs)
def stack_dsigma_profile(lens,
rand,
mask=None,
n_rand=None,
use_dsigma=False,
bootstrap=False,
n_boot=500,
jackknife=True,
n_jobs=None,
n_jk=45):
"""Get the DeltaSigma profile of a sample of lens."""
# Check to see the setup for lens and random
assert np.all(lens.meta['bins'] == rand.meta['bins'])
assert lens.meta['H0'] == rand.meta['H0']
assert lens.meta['Om0'] == rand.meta['Om0']
assert (lens['n_s_tot'] > 0).sum() == len(lens)
assert (rand['n_s_tot'] > 0).sum() == len(rand)
# Apply the mask
lens_use = lens if mask is None else lens[mask]
# Randomly downsample the random objects if necessary
if n_rand is not None and n_rand < len(rand):
rand_use = Table(np.random.choice(rand, size=n_rand, replace=False))
rand_use.meta = rand.meta
else:
rand_use = rand
# Get the stacked lensing profiles
if use_dsigma:
# Configurations for calculating HSC
kwargs = {
'return_table': True,
'shear_bias_correction': True,
'shear_responsivity_correction': True,
'selection_bias_correction': True,
'boost_correction': False,
'random_subtraction': True,
'photo_z_dilution_correction': True,
'rotation': False,
'table_r': rand_use
}
result = excess_surface_density(lens_use, **kwargs)
else:
result = Table()
result['ds'] = dsigma_no_wsys(lens_use, rand_use)
if jackknife:
if n_jk <= 5:
raise Exception(
"Number of jackknife fields is too small, should >5")
if len(lens_use) <= 5:
print("Number of lenses < 5, cannot use Jackknife resampling")
jackknife = False
else:
# Deal with situations with small sample
if len(lens_use) <= n_jk - 5:
n_jk = len(lens) - 5
# Add consistent Jackknife fields to both the lens and random catalogs
add_continous_fields(lens_use, distance_threshold=2)
centers = jackknife_field_centers(lens_use, n_jk, weight='n_s_tot')
add_jackknife_fields(lens_use, centers)
add_jackknife_fields(rand_use, centers)
# Estimate the covariance matrix using Jackknife resampling
cov_jk = dsigma_jk_resample(lens_use, rand_use, n_jobs=n_jobs)
result['ds_err_jk'] = np.sqrt(np.diag(cov_jk))
result.meta['cov_jk'] = cov_jk
result.meta['s2n_jk'] = np.sqrt(
np.dot(result['ds'].T.dot(np.linalg.inv(cov_jk)), result['ds']))
# Estimate the covariance matrix using Bootstrap resampling
if bootstrap:
cov_bt = dsigma_bootstrap(lens_use,
rand_use,
n_boot=n_boot,
n_jobs=n_jobs)
result['ds_err_bt'] = np.sqrt(np.diag(cov_bt))
result.meta['cov_bt'] = cov_bt
result.meta['s2n_bt'] = np.sqrt(
np.dot(result['ds'].T.dot(np.linalg.inv(cov_bt)), result['ds']))
return result