#cat = 'matched'
# Constants
if 'mice' in cat:
h = 0.7
O_matter = 0.25
O_lambda = 0.75
else:
h = 0.7
O_matter = 0.2793
O_lambda = 0.7207
cosmo = LambdaCDM(H0=h * 100., Om0=O_matter, Ode0=O_lambda)
# Import lens catalog
fields, path_lenscat, lenscatname, lensID, lensRA, lensDEC, lensZ, lensDc, rmag, rmag_abs, logmstar =\
utils.import_lenscat(cat, h, cosmo)
print('Lens catalogue:', lenscatname)
# Create normally distributed offsets for the redshifts
if 'offset' in cat:
Sigma_Z = 0.02 * (1 + lensZ)
Sigma_M = [0.29] * len(logmstar)
if 'Z' in cat:
dZlist = np.random.normal(loc=0., scale=Sigma_Z, size=len(Sigma_Z))
lensZ = lensZ + dZlist
print('Added offset to lens redshifts')
print(dZlist)
# Lens selection paramnames = np.array(['lmstellar']) maskvals = np.array([ [10.9,11.1] ]) #maskvals = np.array([ [-inf, inf] ]) srcZmin, srcZmax = [0.1, 0.9] path_output = '/data2/brouwer/shearprofile/EG_results_Sep18/%s'%(cat) ## Pipeline # Import lens catalog fields, path_lenscat, lenscatname, lensRA, lensDEC, lensZ, lensDc, rmag, rmag_abs, logmstar =\ utils.import_lenscat(cat, h) # Define radial bins Rbins, Rcenters, Rmin, Rmax, xvalue = utils.define_Rbins(Runit, Rmin, Rmax, Nbins, Rlog) #for f in range(len(fields)): # Boundaries of the field #fieldRAs, fieldDECs = [[i*20.+5.,(i+1)*20.+5.], [j*20.+5.,(j+1)*20.+5.]] fieldRAs, fieldDECs = [[5., 25.], [5., 25.]] # Selecting the galaxies lying within this field fieldmask_lens = (fieldRAs[0] < lensRA)&(lensRA < fieldRAs[1]) & (fieldDECs[0] < lensDEC)&(lensDEC < fieldDECs[1]) # Importing the sources
path_cat = '/data/users/brouwer/Simulations/Bahamas/BAHAMAS_isolated_new/BAHAMAS_nu0_L400N1024_WMAP9/z_0.250'
catname = '%s/catalog.dat' % path_cat
catalog = np.loadtxt(catname).T[:, lenslist]
# Import galaxy observables (M200, r200, logmstar)
logM200_bhm = catalog[3] # M200 of each galaxy
logM500_bhm = catalog[1] # M500 of each galaxy
r200_bhm = catalog[4] * 1e6 # r200 of each galaxy (in pc)
logmstar_bhm = catalog[5] # Stellar mass of each lens galaxy
## Import MICE catalogue
fields, path_micecat, micecatname, lensID_mice, lensRA_mice, lensDEC_mice, \
lensZ_mice, lensDc_mice, rmag_mice, rmag_abs_mice, logmstar_mice = \
utils.import_lenscat('mice', h, cosmo)
lensDc_mice = lensDc_mice.to('pc').value
lensDa_mice = lensDc_mice / (1. + lensZ_mice)
# Full directory & name of the catalogue
micecatfile = '%s/%s' % (path_micecat, micecatname)
micecat = pyfits.open(micecatfile, memmap=True)[1].data
logmhalo_mice = micecat['lmhalo']
# Import isolated galaxy catalogue
"""
# Plot Bahamas mass histogram
massbins = np.arange(12., 15., 0.25)
plt.hist(M200_bhm, bins=massbins)
O_lambda = 0.7207
cosmo = LambdaCDM(H0=h * 100., Om0=O_matter, Ode0=O_lambda)
# Make use of TeX
rc('text', usetex=True)
# Change all fonts to 'Computer Modern'
rc('font', **{'family': 'serif', 'serif': ['DejaVu Sans']})
# Define number of random catalogues
Nrandoms = 100
## Import lens catalogue
path_lenscat = '/data/users/brouwer/LensCatalogues'
"""
# Data selection
cat = 'gama' # Select the lens catalogue (kids/gama/mice)
fields, path_lenscat, lenscatname, lensID, lensRA, lensDEC, lensZ, lensDc, rmag, rmag_abs, logmstar =\
utils.import_lenscat(cat, h, cosmo)
"""
cat = 'kids'
lenscatfile = '%s/photozs.DR4_trained-on-GAMAequ_ugri+KV_version0.9.fits' % (
path_lenscat)
lenscat = pyfits.open(lenscatfile, memmap=True)[1].data
lensZ = lenscat['z_ANNZ_KV']
masked = lenscat['masked']
masscatfile = '%s/baryonic_mass_catalog_kids.fits' % (path_lenscat)
