def __init__(self, domain):
self.domain = domain
self.reg = domain[0]
self.z_min = float(domain[1])
self.z_max = float(domain[2])
# Load lightcones
self.halo_lightcones = mock.LightCones()
self.halo_lightcones.load_h5([
'halo_lightcone/%s/lightcone_%05d.h5' % (domain[0], n + 1)
for n in range(int(arg.nmocks))
])
self.rand_lightcones = mock.LightCones()
self.rand_lightcones.load_h5([
'rand_lightcone/%s/lightcone_%05d.h5' % (domain[0], n + 1)
for n in range(int(arg.nrands))
])
# Catalogues will be generated from lightcones for given hod
self.galaxy_catalogues = mock.Catalogues()
self.random_catalogues = mock.Catalogues()
self.corr = mock.CorrelationFunction(rp_min=0.5,
rp_max=60.0,
nbin=20,
pi_max=60.0,
pi_nbin=20)
# VIPERS projected correlation function
self.wp_obs = np.loadtxt(
'data/vipers/try1/corr_projected_%s_%s_%s.txt' % domain,
delimiter=' ')
def __init__(self, domain):
self.domain = domain
self.reg = domain[0]
self.z_min = float(domain[1])
self.z_max = float(domain[2])
self.sky = sky[self.reg]
print0('Create Data for ', domain)
# Load lightcones
self.halo_lightcones = mock.LightCones()
self.halo_lightcones.load_h5([
'%s/halo_lightcone/%s/lightcone_%05d.h5' %
(arg.dir, domain[0], n + 1) for n in range(int(arg.nmocks))
if n % mock.comm.n_nodes == mock.comm.rank
])
print0("len halo lightcone %d" % len(self.halo_lightcones))
self.rand_lightcones = mock.LightCones()
self.rand_lightcones.load_h5([
'%s/rand_lightcone/%s/lightcone_%05d.h5' %
(arg.dir, domain[0], n + 1) for n in range(int(arg.nrands))
if n % mock.comm.n_nodes == mock.comm.rank
])
print0("len rand lightcone %d" % len(self.rand_lightcones))
# Catalogues will be generated from lightcones for given hod
self.galaxy_catalogues = mock.Catalogues()
self.random_catalogues = mock.Catalogues()
self.corr = mock.CorrelationFunction(rp_min=0.1,
rp_max=60.0,
nbin=24,
pi_max=60.0,
pi_nbin=20,
ra_min=0.001388889,
dec_min=0.0375)
# VIPERS projected correlation function
self.wp_obs = mock.array.loadtxt(
'%s/data/vipers/run4/0.1/corr_projected_%s_%s_%s.txt' %
((arg.dir, ) + domain))[:, 1]
if mock.comm.rank == 0:
self.covinv = np.load('%s/data/vipers/run4/0.1/covinv_%s_%s_%s.npy'\
% ((arg.dir,) + domain))
print0('Inverse covariance matrix', self.covinv.shape)
else:
self.covinv = None
def read_catalogues(filebase, irange):
cats = mock.Catalogues()
for i in range(int(irange[0]), int(irange[1]) + 1):
filename = '%s%05d.txt' % (filebase, i)
a = np.loadtxt(filename, delimiter=' ', usecols=[1, 2, 3, 6, 7])
cats.append(a, z_min=arg.zmin, z_max=arg.zmax)
return cats
omega_m = param['omega_m']
print('# Setting cosmology: omega_m= %.4f' % omega_m)
print('# redshift-range %f %f' % (arg.zmin, arg.zmax))
#
# Initilise
#
mock.set_loglevel(0)
mock.cosmology.set(omega_m)
mock.distance.init(1.2)
#
# Read catalogues
#
randoms = mock.Catalogues()
filename = '../rands/%s/rand_%s_%05d.txt' % (arg.reg, arg.reg, int(arg.i))
a = np.loadtxt(filename, delimiter=' ', usecols=[1, 2, 3, 6, 7])
randoms.append(a, z_min=arg.zmin, z_max=arg.zmax)
#
# Compute RR paris
#
corr = mock.CorrelationFunction(rp_min=0.1,
rp_max=60.0,
nbin=24,
pi_max=60.0,
pi_nbin=20,
ra_min=0.001388889,
dec_min=0.0375)
# sky
sky = {}
for reg in param['reg']:
sky[reg['name']] = mock.Sky(reg['ra'], reg['dec'], [z_min, z_max])
#
# Set HOD parameters
#
hod = mock.Hod()
hod_param = [11.632682100874081, -0.5706390738948128, 4.904043697780981, -1.0126352684312565, 0.45, 0.9, 1.05, 0.0, 0.9, 0.0, 4.0, 2.0]
hod.set_coef(hod_param)
lightcones = mock.LightCones()
cats = mock.Catalogues()
n = int(arg.n)
def write_catalogue(filename, a):
with open(filename, 'w') as f:
for i in range(a.shape[0]):
f.write('%d %e %e %e %e %e %e %e %e %e %e\n' % (
i,
a[i, 0], a[i, 1], a[i, 2],
a[i, 4], a[i, 3],
a[i, 5], a[i, 6],
a[i, 7], a[i, 10], a[i, 11]))
reg = arg.reg