def plot_difference(ax, color, table_l_1, table_r_1, table_l_2, table_r_2,
survey, survey_2=None, ds_norm=1.0, label=None,
offset=0, lens_bin=0, boost=False):
if len(table_l_1) * len(table_r_1) * len(table_l_2) * len(table_r_2) == 0:
print('Warning: Received empty result to plot.')
return None
if survey_2 is None:
survey_2 = survey
dds = difference(
table_l_1, table_r=table_r_1, table_l_2=table_l_2, table_r_2=table_r_2,
survey_1=survey, survey_2=survey_2, boost=boost) / ds_norm
dds_cov = jackknife_resampling(
difference, table_l_1, table_r=table_r_1, table_l_2=table_l_2,
table_r_2=table_r_2, survey_1=survey, survey_2=survey_2, boost=boost)
if hasattr(ds_norm, 'shape'):
dds_cov = dds_cov / np.outer(ds_norm, ds_norm)
else:
dds_cov = dds_cov / ds_norm**2
if np.all(np.isclose(dds_cov, 0)):
dds_err = np.zeros(len(np.diag(dds_cov)))
else:
dds_err = np.sqrt(np.diag(dds_cov))
plotline, caps, barlinecols = ax.errorbar(
rp * (1 + offset * 0.05), 100 * dds, yerr=100 * dds_err, label=label,
fmt='o', ms=2, color=color, zorder=offset + 100)
plt.setp(barlinecols[0], capstyle='round')
return None
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)
result['ds_err'] = np.sqrt(np.diag(ds_cov))
fname_base = '{}_{}{}{}'.format(
args.survey.lower(), lens_bin,
'_no_dillution_correction' if args.no_dilution_correction else '',
('_nstar_' + args.nstar) if args.nstar is not None else '')
np.savetxt(os.path.join('results', fname_base + '_cov.csv'), ds_cov)
result.write(os.path.join('results', fname_base + '.csv'), overwrite=True)
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)
stacking_kwargs['return_table'] = False
result['ds_err'] = np.sqrt(
np.diag(
jackknife_resampling(excess_surface_density, table_l[mask_l],
**stacking_kwargs)))
result.write('{}_{}.csv'.format(args.survey.lower(), lens_bin))
if len(table_l_part) == 0:
continue
if np.amin(table_l_part['z']) >= np.amax(table_s_part['z_l_max']):
continue
add_precompute_results(table_l_part, table_s_part, rp_bins,
**precompute_kwargs)
# Create the jackknife fields.
table_l_part['n_s_tot'] = np.sum(table_l_part['sum 1'], axis=1)
table_l_part = table_l_part[table_l_part['n_s_tot'] > 0]
table_l_part = add_continous_fields(table_l_part, distance_threshold=2)
centers = jackknife_field_centers(table_l_part, 100)
table_l_part = add_jackknife_fields(table_l_part, centers)
stacking_kwargs['return_table'] = True
result = excess_surface_density(table_l_part, **stacking_kwargs)
stacking_kwargs['return_table'] = False
ds_cov = jackknife_resampling(excess_surface_density, table_l_part,
**stacking_kwargs)
result['ds_err'] = np.sqrt(np.diag(ds_cov))
fname_base = '{}_l{}_s{}'.format(args.survey.lower(), lens_bin,
source_bin)
np.savetxt(os.path.join('results', fname_base + '_cov.csv'), ds_cov)
result.write(os.path.join('results', fname_base + '.csv'),
overwrite=True)
path='data')
except FileNotFoundError:
continue
kwargs = {
'table_r': table_r,
'photo_z_dilution_correction': True,
'boost_correction': True,
'random_subtraction': True,
'return_table': True
}
delta_sigma = excess_surface_density(table_l, **kwargs)
kwargs['return_table'] = False
delta_sigma['delta sigma_err'] = np.sqrt(
np.diag(
jackknife_resampling(excess_surface_density, table_l,
**kwargs)))
color = 'C{}'.format(source_bin)
axarr[0].plot(delta_sigma['rp'],
delta_sigma['f_bias'],
color=color,
ls='-',
label=r"$f_{\rm bias}$" if source_bin == 3 else "")
axarr[0].plot(delta_sigma['rp'],
delta_sigma['b'],
color=color,
ls='--',
label=r"boost" if source_bin == 3 else "")
axarr[1].errorbar(
delta_sigma['rp'] * (1 + (source_bin - lens_bin) * 0.03),