# Import trough catalog
path_troughcat = '/data2/brouwer/MergedCatalogues/trough_catalogues'
troughcatname = '/trough_catalog_gama_absmag_masked.fits'
# Full directory & name of the trough catalogue
troughcatfile = '%s/%s' % (path_troughcat, troughcatname)
troughcat = pyfits.open(troughcatfile, memmap=True)[1].data
# List of the observables of all sources in the KiDS catalogue
Ptheta = troughcat['Ptheta5']
Wtheta = poly_func_weights(Ptheta)
outputnames = ['Ptheta5', 'Wtheta5']
output = [Ptheta, Wtheta]
utils.write_catalog('%s/chi2_trough_weights.fits' % path_troughcat,
np.arange(len(Wtheta)), outputnames, output)
# Calculating covariance of the chi2 weighted trough signal
path_lenssel_weighted = 'No_bins_gama_absmag/Pmasktheta5_0p8_1-Ptheta5_0_0p5_lw-Wtheta5/'
esdfiles_weighted = np.array([
'/%s/%s/%s/%s' %
(path_sheardata, path_lenssel_weighted, path_cosmo, path_filename)
])
data_x, data_y_weighted, error_h_weighted, error_l_weighted = utils.read_esdfiles(
esdfiles_weighted)
covfiles_weighted = np.array([
e.replace('bins_%s.txt' % blind, 'matrix_%s.txt' % blind)
for e in esdfiles_weighted
])
data_weighted = data_y_weighted[0]
# Defining the names of the columns
outputnames = ['RA', 'DEC']
[outputnames.append('Ngaltheta%i' % (theta * 60)) for theta in thetalist]
[outputnames.append('Ngridtheta%i' % (theta * 60)) for theta in thetalist]
[outputnames.append('Pgridtheta%i' % (theta * 60)) for theta in thetalist]
[outputnames.append('rhotheta%i' % (theta * 60)) for theta in thetalist]
[outputnames.append('Ptheta%i' % (theta * 60)) for theta in thetalist]
# Defining the output
output = [gridRA, gridDEC]
[output.append(Ngaltheta[theta, :]) for theta in range(Ntheta)]
[output.append(Ngridtheta[theta, :]) for theta in range(Ntheta)]
[output.append(Pgridtheta[theta, :]) for theta in range(Ntheta)]
[output.append(rhotheta[theta, :]) for theta in range(Ntheta)]
[output.append(Ptheta[theta, :]) for theta in range(Ntheta)]
print('Writing output catalogue...')
filename = '/data2/brouwer/MergedCatalogues/trough_catalogues/mock_trough_catalogue.fits'
utils.write_catalog(filename, gridID, outputnames, output)
# Writing the mean galaxy density information to a text file
field_density = field_galaxies / field_area
field_header = '1: Number of galaxies, 2: Effective area (arcmin^2), 3: Galaxy density (arcmin^-2)'
density_info = np.array([field_galaxies, field_area, field_density])
filename = 'density_info.txt'
np.savetxt(filename, density_info, delimiter=' ', header=field_header)
print('Written:', filename)
for theta in range(len(thetalist)):
troughcatname = 'trough_catalog_%s.fits' % sel[theta]
# Full directory & name of the trough catalogue
troughcatfile = '%s/%s' % (path_troughcat, troughcatname)
troughcat = pyfits.open(troughcatfile, memmap=True)[1].data
print('Creating weight catalog for:', troughcatfile)
# Atheta = poly_func_amps(Ptheta)
# poly_func_amps = np.poly1d(poly_param_amps[theta])
# outputnames.append('Atheta%g'%thetalist[theta])
# output.append(np.abs(Atheta))
# Write weight fits-file
Ptheta = troughcat['Ptheta%g' % thetalist[theta]]
poly_func_weights = np.poly1d(poly_param_weights[theta])
Wtheta = poly_func_weights(Ptheta)
outputnames.append('Ptheta%g' % thetalist[theta])
outputnames.append('Wtheta%g' % thetalist[theta])
output.append(np.abs(Ptheta))
output.append(np.abs(Wtheta))
weightcatname = '%s/amplitude_trough_weights_%s.fits' % (
path_troughcat, sel[0])
utils.write_catalog(weightcatname, np.arange(len(Ptheta)), outputnames,
output)
outputnames = ['RA', 'DEC']
output = [gridRA_tot, gridDEC_tot]
[
outputnames.append('Nmasktheta%g' % (theta * 60))
for theta in thetalist
]
[output.append(Nmasktheta_tot[theta, :]) for theta in range(Ntheta)]
[
outputnames.append('Pmasktheta%g' % (theta * 60))
for theta in thetalist
]
[output.append(Pmasktheta_tot[theta, :]) for theta in range(Ntheta)]
utils.write_catalog(maskfilename, gridID_tot, outputnames, output)
# Save the effective survey area into a text file for later use
np.savetxt(masktextname,
field_area,
header='Total effective field area (in arcmin^2)')
print('Written:', masktextname)
else:
# Import the masked percentage
print('Importing mask from:', maskfilename)
maskcat = pyfits.open(maskfilename, memmap=True)[1].data
Nmasktheta_tot = np.array([
(maskcat['Nmasktheta%g' % (theta * 60)])[0:Ngrid_tot]
for theta in thetalist
# Index of the grid point in the flag list
field_index = sampindex_field[s] + g
# If the point is not flagged (not flagged = 1), flag all overlapping grid points (flagged = 0).
if selected[field_index] == 1:
removeindex = gridoverlap[(sampxgrid == g)
& (gridoverlap != field_index)]
selected[removeindex] = 0.
# If the point is already flagged, do nothing.
else:
pass
# Remove overlapping mask points
selmask = (selected == 1)
print('Selected:', np.sum(selmask), '/', len(selmask))
# Print output to file
filename = '/data2/brouwer/MergedCatalogues/Masks/%s_mask_%s_%gdeg.fits' % (
cat, fieldnames[f], gridspace_mask)
outputnames = ['RA', 'DEC', 'mask']
output = [
RAlist_field[selmask], DEClist_field[selmask], masklist_field[selmask]
]
print('Writing output catalogue...')
utils.write_catalog(filename, np.arange(len(RAlist_field[selmask])),
outputnames, output)