def constraints(params, zs):
cosmo = Class()
cosmo.set(params)
cosmo.compute()
h = cosmo.Hubble(0) * 299792.458
om0 = cosmo.Omega0_m()
#print(cosmo.pars)
zarr2 = cosmo.get_background().get('z')
hz = cosmo.get_background().get('H [1/Mpc]')
hf = interpolate.InterpolatedUnivariateSpline(zarr2[-1:0:-1], hz[-1:0:-1])
chiz = cosmo.get_background().get('comov. dist.')
chif = interpolate.InterpolatedUnivariateSpline(zarr2[-1:0:-1],
chiz[-1:0:-1])
pkz = np.zeros((nk, nz))
pklz2 = np.zeros((nk, nz))
dprime = np.zeros((nk, 1))
zarr = np.linspace(0, zmax, nz)
karr = np.logspace(-3, np.log10(20), nk)
rcollarr = np.zeros(nz)
kcarr = np.zeros(nz)
delz = 0.01
for i in np.arange(nz):
Dz = (cosmo.scale_independent_growth_factor(zarr[i]) /
cosmo.scale_independent_growth_factor(0))
sigz = lambda x: Dz * cosmo.sigma(x, zarr[i]) - 1.192182033080519
if (sigz(1e-5) > 0) & (sigz(10) < 0):
rcollarr[i] = optimize.brentq(sigz, 1e-5, 10)
else:
rcollarr[i] = 0
for j in np.arange(nk):
pkz[j, i] = cosmo.pk(karr[j], zarr[i])
for i in np.arange(nk):
pklz0 = np.log(cosmo.pk(karr[i], zs - delz) / cosmo.pk(karr[i], 0))
pklz1 = np.log(cosmo.pk(karr[i], zs + delz) / cosmo.pk(karr[i], 0))
pklz2[i] = cosmo.pk(karr[i], 0)
dprime[i] = -hf(zs) * np.sqrt(
cosmo.pk(karr[i], zs) / pklz2[i, 0]) * (pklz1 - pklz0) / 4 / delz
#divided by 2 for step size, another for defining D'
w0 = params.get('w0_fld')
wa = params.get('wa_fld')
mt = 5 * np.log10(cosmo.luminosity_distance(zs))
#mt = 5*np.log10(fanal.dlatz(zs, om0, og0, w0, wa))
Rc = (2 * 4.302e-9 * Mc / h**2 / om0)**(1 / 3)
mask = (0 < rcollarr) & (rcollarr < Rc)
kcarr[mask] = 2 * np.pi / rcollarr[mask]
mask = (rcollarr >= Rc)
kcarr[mask] = 2 * np.pi / Rc
#plt.semilogy(zarr, kcarr)
pksmooth = pdf.pkint(karr, zarr, pkz, kcarr)
par2 = {'w0': w0, 'wa': wa, 'Omega_m': om0}
print(par2)
#kmin = conv.kmin(zs, chif, hf)*(-3./2.*hf(0)**2*om0)
kvar = conv.kvar(zs, pksmooth, chif, hf) * (3. / 2. * hf(0)**2 * om0)**2
#sigln = np.log(1+kvar/np.abs(kmin)**2)
#L = pdf.convpdf(kmin, sigln, sig, mfid-mt)
#sigln = np.sqrt(sig**2+(5/np.log(10))**2*kvar)
#L = pdf.gausspdf(sig, mfid-mt)
#lnL = mt/sig
vvar = np.trapz(pklz2[:, 0] * dprime[:, 0]**2,
karr) / 6 / np.pi**2 * (1 -
(1 + zs) / hf(zs) / chif(zs))**2
#var_tot = norm*kvar+sig**2
lnL = -mt**2 / 2
print('Sigmasq = {}, {}, Likelihood = {}'.format(kvar, vvar, lnL))
cosmo.struct_cleanup()
cosmo.empty()
return [mt, kvar, vvar]
#[mt, var_tot]
class Cosmology(object):
"""
Class to hold the basic cosmology and CLASS attributes. This can be initialized by a set of cosmological parameters or a pre-defined cosmology.
Loaded cosmological models:
- **Planck18**: Bestfit cosmology from Planck 2018, using the baseline TT,TE,EE+lowE+lensing likelihood.
- **Quijote**: Fiducial cosmology from the Quijote simulations of Francisco Villaescusa-Navarro et al.
- **Abacus**: Fiducial cosmology from the Abacus simulations of Lehman Garrison et al.
Args:
redshift (float): Desired redshift :math:`z`
name (str): Load cosmology from a list of predetermined cosmologies (see above).
params (kwargs): Any other CLASS parameters. (Note that sigma8 does not seem to be supported by CLASS in Python 3).
Keyword Args:
verb (bool): If true output useful messages througout run-time, default: False.
npoints (int): Number of points to use in the interpolators for sigma^2, default: 100000
"""
loaded_models = {'Quijote':{"h":0.6711,"omega_cdm":(0.3175 - 0.049)*0.6711**2,
"Omega_b":0.049, "n_s":0.9624,
"N_eff":3.046, "A_s":2.134724e-09}, #"sigma8":0.834,
'Abacus':{"h":0.6726,"omega_cdm":0.1199,
"omega_b":0.02222,"n_s":0.9652,"A_s":2.135472e-09,#"sigma8":0.830,
"N_eff":3.046},
'Planck18':{"h":0.6732,"omega_cdm":0.12011,"omega_b":0.022383,
"n_s":0.96605,"A_s":2.042644e-09}}#,"sigma8":0.8120}}
def __init__(self,redshift,name="",verb=False,npoints=int(1e5),**params):
"""
Initialize the cosmology class with cosmological parameters or a defined model.
"""
## Load parameters into a dictionary to pass to CLASS
class_params = dict(**params)
if len(name)>0:
if len(params.items())>0:
raise Exception('Must either choose a preset cosmology or specify parameters!')
if name in self.loaded_models.keys():
if verb: print('Loading the %s cosmology at z = %.2f'%(name,redshift))
loaded_model = self.loaded_models[name]
for key in loaded_model.keys():
class_params[key] = loaded_model[key]
else:
raise Exception("This cosmology isn't yet implemented")
else:
if len(params.items())==0:
if verb: print('Using default CLASS cosmology')
for name, param in params.items():
class_params[name] = param
## # Check we have the correct parameters
if 'sigma8' in class_params.keys() and 'A_s' in class_params.keys():
raise NameError('Cannot specify both A_s and sigma8!')
## Define other parameters
self.z = redshift
self.a = 1./(1.+redshift)
if 'output' not in class_params.keys():
class_params['output']='mPk'
if 'P_k_max_h/Mpc' not in class_params.keys() and 'P_k_max_1/Mpc' not in class_params.keys():
class_params['P_k_max_h/Mpc']=300.
if 'z_pk' in class_params.keys():
assert class_params['z_pk']==redshift, "Can't pass multiple redshifts!"
else:
class_params['z_pk']=redshift
## Load CLASS and set parameters
if verb: print('Loading CLASS')
self.cosmo = Class()
self.cosmo.set(class_params)
self.cosmo.compute()
self.h = self.cosmo.h()
self.name = name
self.npoints = npoints
self.verb = verb
## Check if we're using neutrinos here
if self.cosmo.Omega_nu>0.:
if self.verb: print("Using a neutrino fraction of Omega_nu = %.3e"%self.cosmo.Omega_nu)
self.use_neutrinos = True
# Define neutrino mass fraction
self.f_nu = self.cosmo.Omega_nu/self.cosmo.Omega_m()
if self.cosmo.Neff()>3.5:
print("N_eff > 3.5, which seems large (standard value: 3.046). This may indicate that N_ur has not been set.")
else:
if self.verb: print("Assuming massless neturinos.")
self.use_neutrinos = False
## Create a vectorized sigma(R) function from CLASS
if self.use_neutrinos:
self.vector_sigma_R = np.vectorize(lambda r: self.cosmo.sigma_cb(r/self.h,self.z))
else:
self.vector_sigma_R = np.vectorize(lambda r: self.cosmo.sigma(r/self.h,self.z))
# get density in physical units at z = 0
# rho_critical is in Msun/h / (Mpc/h)^3 units
# rhoM is in **physical** units of Msun/Mpc^3
self.rho_critical = ((3.*100.*100.)/(8.*np.pi*6.67408e-11)) * (1000.*1000.*3.085677581491367399198952281E+22/1.9884754153381438E+30)
self.rhoM = self.rho_critical*self.cosmo.Omega0_m()
def compute_linear_power(self,kh,kh_min=0.,with_neutrinos=False):
"""Compute the linear power spectrum from CLASS for a vector of input k.
If set, we remove any modes below some minimum k.
Args:
kh (float, np.ndarray): Wavenumber or vector of wavenumbers (in h/Mpc units) to compute linear power with.
Keyword Args:
kh_min (float): Value of k (in h/Mpc units) below which to set :math:`P(k) = 0`, default: 0.
with_neutrinos (bool): If True, return the full matter power spectrum, else return the CDM+baryon power spectrum (which is generally used in the halo model). Default: False.
Returns:
np.ndarray: Linear power spectrum in :math:`(h^{-1}\mathrm{Mpc})^3` units
"""
if type(kh)==np.ndarray:
# Define output vector and filter modes with too-small k
output = np.zeros_like(kh)
filt = np.where(kh>kh_min)
N_k = len(filt[0])
# Compute Pk using CLASS (vectorized)
if not hasattr(self,'vector_linear_power'):
## NB: This works in physical 1/Mpc units so we convert here
if self.use_neutrinos:
# Here we need both the CDM+baryon (cb) power spectra and the full matter power spectra
# The CDM+baryon spectrum is used for the halo model parts, and the residual (matter-cb) added at the end
self.vector_linear_power = np.vectorize(lambda k: self.cosmo.pk_cb_lin(k*self.h,self.z)*self.h**3.)
self.vector_linear_power_total = np.vectorize(lambda k: self.cosmo.pk_lin(k*self.h,self.z)*self.h**3.)
else:
self.vector_linear_power = np.vectorize(lambda k: self.cosmo.pk_lin(k*self.h,self.z)*self.h**3.)
if self.use_neutrinos and with_neutrinos:
output[filt] = self.vector_linear_power_total(kh[filt])
else:
output[filt] = self.vector_linear_power(kh[filt])
return output
else:
if kh<kh_min:
return 0.
else:
if self.use_neutrinos and with_neutrinos:
return self.vector_linear_power_total(kh)
else:
return self.vector_linear_power(kh)
def sigma_logM_int(self,logM_h):
"""Return the value of :math:`\sigma(M,z)` using the prebuilt interpolators, which are constructed if not present.
Args:
logM (np.ndarray): Input :math:`\log_{10}(M/h^{-1}M_\mathrm{sun})`
Returns:
np.ndarray: :math:`\sigma(M,z)`
"""
if not hasattr(self,'sigma_logM_int_func'):
self._interpolate_sigma_and_deriv(npoints=self.npoints)
return self._sigma_logM_int_func(logM_h)
def dlns_dlogM_int(self,logM_h):
"""Return the value of :math:`d\ln\sigma/d\log M` using the prebuilt interpolators, which are constructed if not present.
Args:
logM (np.ndarray): Input :math:`\log_{10}(M/h^{-1}M_\mathrm{sun})`
Returns:
np.ndarray: :math:`d\ln\sigma/d\log M`
"""
if not hasattr(self,'dlns_dlogM_int_func'):
self._interpolate_sigma_and_deriv(npoints=self.npoints)
return self._dlns_dlogM_int_func(logM_h)
def _sigmaM(self,M_h):
"""Compute :math:`\sigma(M,z)` from CLASS as a vector function.
Args:
M_h (np.ndarray): Mass in :math:`h^{-1}M_\mathrm{sun}` units.
z (float): Redshift.
Returns:
np.ndarray: :math:`\sigma(M,z)`
"""
# convert to Lagrangian radius
r_h = np.power((3.*M_h)/(4.*np.pi*self.rhoM),1./3.)
sigma_func = self.vector_sigma_R(r_h)
return sigma_func
def _interpolate_sigma_and_deriv(self,logM_h_min=6,logM_h_max=17,npoints=int(1e5)):
"""Create an interpolator function for :math:`d\ln\sigma/d\log M` and :math:`sigma(M)`.
NB: This has no effect if the interpolator has already been computed.
Keyword Args:
logM_min (float): Minimum mass in :math:`\log_{10}(M/h^{-1}M_\mathrm{sun})`, default: 6
logM_max (float): Maximum mass in :math:`\log_{10}(M/h^{-1}M_\mathrm{sun})`, default 17
npoints (int): Number of sampling points, default 100000
"""
if not hasattr(self,'_sigma_logM_int_func'):
if self.verb: print("Creating an interpolator for sigma(M) and its derivative.")
## Compute log derivative by interpolation and numerical differentiation
# First compute the grid of M and sigma
M_h_grid = np.logspace(logM_h_min,logM_h_max,10000)
all_sigM = self._sigmaM(M_h_grid)
logM_h_grid = np.log10(M_h_grid)
# Define ln(sigma) and numerical derivatives
all_lns = np.log(all_sigM)
all_diff = -np.diff(all_lns)/np.diff(logM_h_grid)
mid_logM_h = 0.5*(logM_h_grid[:-1]+logM_h_grid[1:])
self._sigma_logM_int_func = interp1d(logM_h_grid,all_sigM)
self._dlns_dlogM_int_func = interp1d(mid_logM_h,all_diff)
def _h_over_h0(self):
"""Return the value of :math:`H(z)/H(0)` at the class redshift
Returns:
float: :math:`H(z)/H(0)`
"""
Omega0_k = 1.-self.cosmo.Omega0_m()-self.cosmo.Omega_Lambda()
Ea = np.sqrt((self.cosmo.Omega0_m()+self.cosmo.Omega_Lambda()*pow(self.a,-3)+Omega0_k*self.a)/pow(self.a,3))
return Ea
def _Omega_m(self):
"""Return the value of :math:`\Omega_m(z)` at the class redshift
