def standard_mocks(i0, i1):
''' construct standard test mocks
'''
for i_fid in range(i0, i1 + 1):
print('Fiducial %i' % i_fid)
# read in halo catalog
halos = Halos.Quijote_fiducial_HR(i_fid, z=0.5)
for i_hod in range(5):
print(' HOD %i' % i_hod)
fgal = os.path.join(
dat_dir, 'tests',
'hod.quijote_fid%i.z0p5.cmass_sgc.%i.bf' % (i_fid, i_hod))
fhod = fgal.replace('.bf', '.npy')
if not os.path.isfile(fhod) or not os.path.isdir(fgal):
# sample HOD parameters
theta_hod = sample_HOD()
np.save(fhod, theta_hod)
# populate with HOD
hod = Galaxies.hodGalaxies(halos, theta_hod, seed=0)
# apply forward model (footprint, veto mask fiber collisions)
gals = FM.BOSS(hod,
sample='cmass-south',
seed=0,
veto=True,
fiber_collision=True,
silent=True)
# save to file
gals.save(fgal)
return None
def assemblybias_mocks(i0, i1):
''' construct assembly bias test mocks
'''
theta_fid = Galaxies.thetahod_literature('reid2014_cmass')
abiases = np.array([
-1., -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.1, 0.2,
0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.
])
for i_fid in range(i0, i1 + 1):
print('Fiducial %i' % i_fid)
halos = Halos.Quijote_fiducial_HR_Rockstar(i_fid, z=0.5)
_Z07AB = Galaxies.AssembiasZheng07Model()
Z07AB = _Z07AB.to_halotools(halos.cosmo,
halos.attrs['redshift'],
halos.attrs['mdef'],
sec_haloprop_key='halo_nfw_conc')
for i_ab, abias in enumerate(abiases):
# HOD parameters with assembly bias
_theta_ab = theta_fid.copy()
_theta_ab['mean_occupation_centrals_assembias_param1'] = abias
_theta_ab['mean_occupation_satellites_assembias_param1'] = abias
# populate with HOD
hod_ab = halos.populate(Z07AB, seed=0, **_theta_ab)
gals_ab = FM.BOSS(hod_ab,
sample='cmass-south',
seed=0,
veto=True,
fiber_collision=True,
silent=True)
fgal = os.path.join(
dat_dir, 'tests',
'hod.quijote_fid_hr%i.z0p5.cmass_sgc.abias%i.bf' %
(i_fid, i_ab))
gals_ab.save(fgal)
return None
def sample_HOD():
''' sample HOD value based on priors set by Parejko+(2013)
'''
hod_min = np.array([13.2, 0.4, 13.1, 14., 0.7])
dhod = np.array([0.15, 0.1, 0.4, 0.3, 0.4])
_hod = hod_min + dhod * np.random.uniform(size=(5))
return {'logMmin': _hod[0], 'sigma_logM': _hod[1], 'logM0': _hod[2], 'logM1': _hod[3], 'alpha': _hod[4]}
dat_dir = '/tigress/chhahn/simbig/lowz/'
for i_lhc in range(i0, i1):
print('LHC %i' % i_lhc)
# read in halo catalog
halos = Halos.Quijote_LHC_HR(i_lhc, z=0.5)
for i_hod in range(5):
print(' HOD %i' % i_hod)
fgal = os.path.join(dat_dir, 'hod.quijote_LH%i.z0p5.lowz_sgc.%i.bf' % (i_lhc, i_hod))
fhod = fgal.replace('.bf', '.npy')
if not os.path.isfile(fhod) or not os.path.isdir(fgal):
# sample HOD parameters
theta_hod = sample_HOD()
np.save(fhod, theta_hod)
# populate with HOD
hod = Galaxies.hodGalaxies(halos, theta_hod, seed=0)
# apply forward model (footprint, veto mask fiber collisions)
gals = FM.BOSS(hod, sample='lowz-south', seed=0, veto=True, fiber_collision=True, silent=True)
# save to file
gals.save(fgal)