def matterps2corr2esd(redshift,cosmology):
z = redshift
H_null,Ombh2,Omch2=cosmology
pars = camb.CAMBparams()
pars.set_cosmology(H0=H_null,ombh2=Ombh2,omch2=Omch2)
pars.set_dark_energy()
pars.InitPower.set_params(ns=nps)
pars.set_matter_power(redshifts=[0.0,z],kmax=2.0)
#Linear spectra--------
pars.NonLinear=model.NonLinear_none
results =camb.get_results(pars)
kh,znon,pk =results.get_matter_power_spectrum(minkh=1e-4,maxkh=1000.0,npoints=1024)
xi =mcfit.P2xi(kh,l=0)
#Calculate corr from PS------
nxx = 50
Rmin = -2.0
Rmax = 2.0
rr = np.logspace(Rmin,Rmax,nxx)
rx,corrfunc =xi(pk,extrap=True)
corr = np.interp(rr,rx,corrfunc[0,:])
#Calculate ESD from corr------
nxx = 10
Rp = np.linspace(0.03,20.,nxx)
ESD = np.zeros(nxx)
for i in range(nxx):
tmp1 = integrate.quad(simR,Rp[i]+0.001,90.0,args=(rr,corr,Rp[i]),epsabs=1e-4)
tmp2 = integrate.quad(simLR,0.001,Rp[i],args=(rr,corr,Rp[i]),epsabs=1e-4)
ESD[i] = (4.0*tmp2[0]/Rp[i]/Rp[i]-2.0*tmp1[0])*omega_m*h #Luo et al 2017ApJ 836 38L EQ. 39-40
return {'Rp':Rp,'ESD':ESD}
def pk_to_xi(k, Pk, ell=0, extrap=True):
r"""
Return a callable function returning the correlation function multipole of
degree :math:`\ell`, as computed from the Fourier transform of the input
:math:`k` and :math:`P_\ell(k)` arrays.
This uses the :mod:`mcfit` package perform the FFT.
Parameters
----------
k : array_like
wavenumbers where ``Pk`` is evaluated
Pk : array_like
the array holding the power spectrum multipole values
ell : int
multipole degree of the input power spectrum and the output correlation
function; monopole by default
extrap : bool, optional
whether to extrapolate the power spectrum with a power law; can improve
the smoothness of the FFT
Returns
-------
InterpolatedUnivariateSpline :
a spline holding the interpolated correlation function values
"""
xi = mcfit.P2xi(k, l=ell, lowring=True)
rr, CF = xi(Pk, extrap=extrap)
return InterpolatedUnivariateSpline(rr, CF)
def pk_to_xi(k, Pk, extrap=True):
r"""
Return a callable function returning the correlation function, as computed
from the Fourier transform of the input :math:`k` and :math:`P(k)` arrays.
This uses the :mod:`mcfit` package perform the FFT.
Parameters
----------
k : array_like
wavenumbers where ``Pk`` is evaluated
Pk : array_like
the array holding the power spectrum values
extrap : bool, optional
whether to extrapolate the power spectrum with a power law; can improve
the smoothness of the FFT
Returns
-------
InterpolatedUnivariateSpline :
a spline holding the interpolated correlation function values
"""
xi = mcfit.P2xi(k)
rr, CF = xi(Pk, extrap=extrap)
return InterpolatedUnivariateSpline(rr, CF)
def camb_corr_func(zs, read=True):
pks = power_spec_at_zs(zs, read=read)
rs, cfs = mcfit.P2xi(k_grid, lowring=True)(pks, axis=1)
return rs, cfs
def from_power_spectrum(cls, ks, zs, power_spectra):
rs, xis = mcfit.P2xi(ks, lowring=True)(power_spectra, extrap=True)
return cls(rs, zs, xis)