def readcat(path, subsample=False):
# Only used to compute concentration etc from halo mass
# thus no need to be accurate.
CP = cosmology.Cosmology(Omega0_cdm=0.3, h=0.677, Omega0_b=.05)
cat = BigFileCatalog(path, dataset='LL-0.200')
if subsample:
cat = cat[:subsample]
M0 = cat.attrs['M0'] * 1e10
if cat.comm.rank == 0:
cat.logger.info("mass of a particle %e" % M0)
cat['Mass'] = cat['Length'] * M0
if 'Aemit' in cat.columns:
redshift = 1 / cat['Aemit'] - 1
else:
redshift = 1 / cat.attrs['Time'] - 1
cat['conc'] = transform.HaloConcentration(
cat['Mass'], CP, redshift).compute(scheduler="single-threaded")
# proper to comoving
cat['rvir'] = transform.HaloRadius(cat['Mass'], CP,
redshift).compute() * (1 + redshift)
cat['vdisp'] = transform.HaloVelocityDispersion(cat['Mass'], CP,
redshift).compute()
if 'Aemit' not in cat.columns:
cat['Aemit'] = cat.attrs['Time'][0]
mean, std = stat(cat['Aemit'].compute(), cat.comm)
if cat.comm.rank == 0:
cat.logger.info("Aemit mean = %g std = %g" % (mean, std))
mean, std = stat(cat['Mass'].compute(), cat.comm)
if cat.comm.rank == 0:
cat.logger.info("mass mean, std = %g, %g" % (mean, std))
mean, std = stat(cat['conc'].compute(), cat.comm)
if cat.comm.rank == 0:
cat.logger.info("conc mean, std = %g, %g" % (mean, std))
mean, std = stat(cat['rvir'].compute(), cat.comm)
if cat.comm.rank == 0:
cat.logger.info("rvir mean, std = %g, %g" % (mean, std))
mean, std = stat(cat['vdisp'].compute(), cat.comm)
if cat.comm.rank == 0:
cat.logger.info("vdisp mean, std = %g, %g" % (mean, std))
cat.attrs['BoxSize'] = numpy.ones(3) * cat.attrs['BoxSize'][0]
return cat
def __init__(self,
h=0.67556,
T0_cmb=2.7255,
Omega0_b=0.0482754208891869,
Omega0_cdm=0.26377065934278865,
N_ur=None,
m_ncdm=[0.06],
P_k_max=10.0,
P_z_max=100.0,
sigma8=0.8225,
gauge='synchronous',
n_s=0.9667,
nonlinear=False):
# input parameters
kw_cosmo = locals()
kw_cosmo.pop('self')
for kw, val in kw_cosmo.items():
print(f'{kw:10s}: {val}')
sigma8 = kw_cosmo.pop('sigma8')
cosmo = cosmology.Cosmology(**kw_cosmo).match(sigma8=sigma8)
# --- cosmology calculators
redshift = 0.0
delta_crit = 1.686 # critical overdensity for collapse from Press-Schechter
DH = (
cosmo.H0 / cosmo.C
) # in units of h/Mpc, eq. 4 https://arxiv.org/pdf/astro-ph/9905116.pdf
Omega0_m = cosmo.Omega0_m
k_ = np.logspace(-10, 10, 10000)
Plin_ = cosmology.LinearPower(cosmo, redshift,
transfer='CLASS') # P(k) in (Mpc/h)^3
self.Plin = IUS(k_, Plin_(k_), ext=1)
Tlin_ = cosmology.transfers.CLASS(
cosmo, redshift) # T(k), normalized to one at k=0
tk_ = Tlin_(k_)
is_good = np.isfinite(tk_)
self.Tlin = IUS(k_[is_good], tk_[is_good], ext=1)
self.Dlin = cosmo.scale_independent_growth_factor # D(z), normalized to one at z=0
self.f = cosmo.scale_independent_growth_rate # dlnD/dlna
self.Dc = cosmo.comoving_distance
self.h = cosmo.efunc
self.inv_pi = 2.0 / np.pi
# alpha_fnl: equation 2 of Mueller et al 2017
self.alpha_fnl = 3.0 * delta_crit * Omega0_m * (DH**2)
def init_plin(self):
h = 0.676
Omega_nu = 0.00140971
Omega0_m = 0.31
Omega0_b = 0.022 / h**2
Omega0_cdm = Omega0_m - Omega0_b - Omega_nu
n_s = 0.96
sigma8 = 0.824
cosmo = cosmology.Cosmology(h=h,
Omega0_b=Omega0_b,
Omega0_cdm=Omega0_cdm,
n_s=n_s)
cosmo.match(sigma8=sigma8)
redshift = 0.57
self.Plin = LinearPower(cosmo, redshift, transfer='CLASS')
def generate_data(x):
redshift = 0.0
r_vals = np.linspace(50, 140, 10)
extra_input = {'redshift': redshift, 'r_vals': r_vals}
n_data = x.shape[1]
y = np.empty((len(r_vals), n_data))
for i in range(n_data):
print(i)
om, s8, ob = x[:, i]
ocdm = om - ob
m_ncdm = [] #no massive neutrinos
cosmo = cosmology.Cosmology(Omega0_b=ob,
Omega0_cdm=ocdm,
m_ncdm=m_ncdm)
cosmo = cosmo.match(sigma8=s8)
plin = cosmology.LinearPower(cosmo, redshift, transfer='EisensteinHu')
cf = cosmology.correlation.CorrelationFunction(plin)
y[:, i] = cf(r_vals)
return y, extra_input
def bias_model_lrg(z):
""" arxiv.org/abs/2001.06018
"""
kw_cosmo = dict(h=0.67556,
T0_cmb=2.7255,
Omega0_b=0.0482754208891869,
Omega0_cdm=0.26377065934278865,
N_ur=None,
m_ncdm=[0.06],
P_k_max=10.0,
P_z_max=100.0,
sigma8=0.8225,
gauge='synchronous',
n_s=0.9667,
nonlinear=False)
sigma8 = kw_cosmo.pop('sigma8')
cosmo = cosmology.Cosmology(**kw_cosmo).match(sigma8=sigma8)
Dlin = cosmo.scale_independent_growth_factor # D(z), normalized to one at z=0
return 1.42 / Dlin(z)
def plot_spectrum_nowiggle_comparison():
from matplotlib import pyplot
k,pklin = load_pklin()
spectrum_nowiggle = SpectrumNoWiggle(k=k)
cosmo_kwargs = dict(Omega_m=0.31,omega_b=0.022,h=0.676,n_s=0.97)
spectrum_nowiggle.run_terms(pk=pklin,**cosmo_kwargs)
tk = spectrum_nowiggle.transfer()
from nbodykit.cosmology.power.transfers import NoWiggleEisensteinHu
from nbodykit import cosmology
cosmo_kwargs = dict(Omega_m=0.31,omega_b=0.022,h=0.676,sigma8=0.8,n_s=0.97,N_ur=2.0328,m_ncdm=[0.06])
cosmo_kwargs['Omega0_b'] = cosmo_kwargs.pop('omega_b')/cosmo_kwargs['h']**2
Omega0_m = cosmo_kwargs.pop('Omega_m')
sigma8 = cosmo_kwargs.pop('sigma8')
cosmo = cosmology.Cosmology(**cosmo_kwargs).match(Omega0_m=Omega0_m).match(sigma8=sigma8)
eh = NoWiggleEisensteinHu(cosmo,redshift=0.)(k)
pyplot.loglog(k,pklin,label='$P_{\\rm lin}$')
pyplot.loglog(k,tk,label='$P_{\\rm nowiggle}$')
pyplot.loglog(k,eh,label='nbodykit')
pyplot.legend()
pyplot.show()
grid_mesh = 1024
Boxsize = cata_6411.attrs['BoxSize'] / 1000.
print(r"Nmesh = {:.0f}, a_sqrt = {:.0f}, BoxSize = {:.0f}".format(
grid_mesh, a_sqrt, Boxsize))
mesh_6411 = cata_6411.to_mesh(Nmesh=grid_mesh,
BoxSize=Boxsize,
resampler='cic')
mesh_6411 = mesh_6411.compute()
print(mesh_6411.shape)
# xi_6411 = FFTCorr(mesh_6411, mode='1d')
redshift = cata_6411.attrs['Redshift']
cosmo = cosmology.Cosmology(
h=cata_6411.attrs['HubbleParam'],
P_k_max=200).match(Omega0_m=cata_6411.attrs['Omega0'])
pk_cosmo = cosmology.LinearPower(cosmo, redshift, transfer='EisensteinHu')
def VectorProjection(vector, direction):
r"""
Vector components of given vectors in a given direction.
.. math::
\mathbf{v}_\mathbf{d} &= (\mathbf{v} \cdot \hat{\mathbf{d}}) \hat{\mathbf{d}} \\
\hat{\mathbf{d}} &= \frac{\mathbf{d}}{\|\mathbf{d}\|}
Parameters
----------
cata_fa = BigFileCatalog('/home/yuyu22/testfast/B2T10/snapshot/fastpm_1.0000/',dataset='1/',header='Header/')
cata_fa.attrs
grid_mesh = 1024
print("Nmesh = ", grid_mesh)
Boxsize = 500
mesh_fa = cata_fa.to_mesh(Nmesh=grid_mesh, BoxSize=Boxsize, resampler='cic')
mesh_fa = mesh_fa.compute()
print(mesh_fa.shape)
# xi_fa = FFTCorr(mesh_fa, mode='1d')
redshift = 0.0;
cosmo = cosmology.Cosmology(h=0.6898, P_k_max=200).match(Omega0_m=0.2905)
pk_cosmo = cosmology.LinearPower(cosmo, redshift, transfer='EisensteinHu')
def VectorProjection(vector, direction):
r"""
Vector components of given vectors in a given direction.
.. math::
\mathbf{v}_\mathbf{d} &= (\mathbf{v} \cdot \hat{\mathbf{d}}) \hat{\mathbf{d}} \\
\hat{\mathbf{d}} &= \frac{\mathbf{d}}{\|\mathbf{d}\|}
Parameters
----------
vector : array_like, (..., D)
array of vectors to be projected