def test_linear():
# initialize the power
c = Cosmology().match(sigma8=0.82)
P = LinearPower(c, redshift=0, transfer='CLASS')
# check velocity dispersion
assert_allclose(P.velocity_dispersion(), 5.898, rtol=1e-3)
# test sigma8
assert_allclose(P.sigma_r(8.), c.sigma8, rtol=1e-5)
# change sigma8
P.sigma8 = 0.80
c = c.match(sigma8=0.80)
assert_allclose(P.sigma_r(8.), P.sigma8, rtol=1e-5)
# change redshift and test sigma8(z)
P.redshift = 0.55
assert_allclose(P.sigma_r(8.), c.sigma8_z(P.redshift), rtol=1e-5)
# desired wavenumbers (in h/Mpc)
k = numpy.logspace(-3, 2, 500)
# initialize EH power
P1 = LinearPower(c, redshift=0., transfer="CLASS")
P2 = LinearPower(c, redshift=0., transfer='EisensteinHu')
P3 = LinearPower(c, redshift=0., transfer='NoWiggleEisensteinHu')
# check different transfers (very roughly)
Pk1 = P1(k)
Pk2 = P2(k)
Pk3 = P3(k)
assert_allclose(Pk1 / Pk1.max(), Pk2 / Pk2.max(), rtol=0.1)
assert_allclose(Pk1 / Pk1.max(), Pk3 / Pk3.max(), rtol=0.1)
# also try scalar
Pk = P(0.1)
def run_ConvolvedFFTPower(galaxy_path,
random_path,
output_path,
use_systot=True,
zmin=None,
zmax=None,
compmin=0.5,
cosmology=None,
boxsize=None,
nmesh=512,
dk=0.002,
poles=[0, 2, 4],
kmax=None,
comm=None,
return_pk=False):
if isinstance(nmesh, list):
if len(nmesh)==1:
nmesh=nmesh[0]
if isinstance(boxsize, list):
if len(boxsize)==1:
boxsize=boxsize[0]
if (cosmology is None) or (cosmology=='boss'):
# the fiducial BOSS DR12 cosmology
# see Alam et al. https://arxiv.org/abs/1607.03155
cosmo = Cosmology(h=0.676).match(Omega0_m=0.31)
elif cosmology=='ezmock':
_h = 0.6777
_Ob0 = 0.048206
_Ocdm0 = 0.307115 - _Ob0
cosmo = Cosmology(h=_h, Omega_b=_Ob0, Omega_cdm=_Ocdm0,
m_ncdm=None, n_s=0.9611, T_cmb=2.7255).match(sigma8=0.8225)
elif cosmology=='boss2':
h = 0.676
redshift = 1.48
cos = Cosmology(h=0.676,Omega0_b=0.022/h**2,n_s=0.97).match(Omega0_m=0.31)
cosmo = cos.match(sigma8=0.8)
elif isinstance(cosmology, Cosmology):
cosmo = cosmology
else:
raise NotImplementedError(f'{cosmology} is not implemented')
data = FITSCat(galaxy_path)
random = FITSCat(random_path)
# select based on redshift and comp_BOSS
kwargs = dict(compmin=compmin, zmin=zmin, zmax=zmax)
data = data.trim_redshift_range(**kwargs)
random = random.trim_redshift_range(**kwargs)
# sky to xyz
data.sky_to_xyz(cosmo)
random.sky_to_xyz(cosmo)
# prepare weights
data.apply_weight(use_systot=use_systot)
random.apply_weight(use_systot=True) # always weight randoms by systot
# combine the data and randoms into a single catalog
mesh_kwargs = {'interlaced': True,'window': 'tsc'}
fkp = nb.FKPCatalog(data, random, nbar='NZ', BoxSize=boxsize)
mesh = fkp.to_mesh(Nmesh=nmesh, fkp_weight='WEIGHT_FKP', **mesh_kwargs)
r = nb.ConvolvedFFTPower(mesh, poles=poles, dk=dk, kmin=0.0, kmax=kmax)
comm.Barrier()
if comm.rank == 0:
for parameter in ['sigma8', 'Omega0_m', 'h', 'n_s',
'Omega0_b', 'Omega0_lambda']:
r.attrs[parameter] = getattr(cosmo, parameter)
if comm.rank == 0:
output_dir = os.path.dirname(os.path.abspath(output_path))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
r.save(output_path)
if return_pk:
if comm.rank == 0:
return r
