def loglkl(self, params):
cosmo = Class()
cosmo.set(params)
cosmo.compute()
chi2 = 0.
# for each point, compute angular distance da, radial distance dr,
# volume distance dv, sound horizon at baryon drag rs_d,
# theoretical prediction and chi2 contribution
for i in range(self.num_points):
da = cosmo.angular_distance(self.z[i])
dr = self.z[i] / cosmo.Hubble(self.z[i])
dv = pow(da * da * (1 + self.z[i]) * (1 + self.z[i]) * dr, 1. / 3.)
rs = cosmo.rs_drag()
if self.type[i] == 3:
theo = dv / rs
elif self.type[i] == 4:
theo = dv
elif self.type[i] == 5:
theo = da / rs
elif self.type[i] == 6:
theo = 1. / cosmo.Hubble(self.z[i]) / rs
elif self.type[i] == 7:
theo = rs / dv
chi2 += ((theo - self.data[i]) / self.error[i]) ** 2
# return ln(L)
# lkl = - 0.5 * chi2
# return -2ln(L)
lkl = chi2
return lkl
def get_bao_rs_dV(zs,params=None,engine='camb',de='ppf'):
#FIXME: camb and class only agree at 3% level!!!
import camb
params = map_params(params,engine=engine)
if engine=='camb':
pars = set_camb_pars(params=params,de=de)
results = camb.get_results(pars)
retval = results.get_BAO(zs,pars)[:,0]
elif engine=='class':
from classy import Class
zs = np.asarray(zs)
cosmo = Class()
params['output'] = ''
cosmo.set(params)
cosmo.compute()
Hzs = np.array([cosmo.Hubble(z) for z in zs])
D_As = np.array([cosmo.angular_distance(z) for z in zs])
D_Vs = ((1+zs)**2 * D_As**2 * zs/Hzs)**(1/3.)
retval = cosmo.rs_drag()/D_Vs
cosmo.struct_cleanup()
cosmo.empty()
return retval
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill, Andy Scacco
"""
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
def __call__(self,
**kwargs):
self.model.set(**kwargs)
self.model.compute()
ell = arange(l_max_scalar+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max_scalar)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill, Andy Scacco
"""
#{cosmoslik name : class name} - This needs to be done even for variables with the same name (because of for loop in self.model.set)!
name_mapping = {#'As':'A_s',
#'ns':'n_s',
#'r':'r',
'custom1':'custom1',
'custom2':'custom2',
'custom3':'custom3',
#'nt':'n_t',
'ombh2':'omega_b',
'omch2':'omega_cdm',
'omnuh2':'omega_ncdm',
'tau':'tau_reio',
'H0':'H0',
'massive_neutrinos':'N_ncdm',
'massless_neutrinos':'N_ur',
'Yp':'YHe',
'pivot_scalar':'k_pivot',
'omk':'Omega_k',
'l_max_scalar':'l_max_scalars',
'l_max_tensor':'l_max_tensors',
'Tcmb':'T_cmb'
}
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
#def __call__(self,
# **kwargs):
# d={}
# for k, v in kwargs.iteritems():
# if k in self.name_mapping and v is not None:
# d[self.name_mapping[k]]=v
# else:
# d[k]=v
#def __call__(self,
#ombh2,
#omch2,
#H0,
#As,
#ns,
#custom1,
#custom2,
#custom3,
#tau,
#w=None,
#r=None,
#nrun=None,
#omk=0,
#Yp=None,
#Tcmb=2.7255,
#massless_neutrinos=3.046,
#l_max_scalar=3000,
#l_max_tensor=3000,
#pivot_scalar=0.05,
#outputs=[],
#**kwargs):
#print kwargs
def __call__(self,**kwargs):
#print kwargs
#print kwargs['classparamlist']
#print kwargs['d']
d={}
for k,v in kwargs.iteritems():
if k in kwargs['classparamlist']:
if k in self.name_mapping and v is not None:
d[self.name_mapping[k]]=v
else:
d[k]=v
#d['P_k_ini type']='external_Pk'
#d['modes'] = 's,t'
self.model.set(**d)
l_max = d['l_max_scalars']
Tcmb = d['T_cmb']
#print l_max
#print d
self.model.compute()
ell = arange(l_max+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill, Andy Scacco
"""
#{cosmoslik name : class name} - This needs to be done even for variables with the same name (because of for loop in self.model.set)!
name_mapping = {'As':'A_s',
'ns':'n_s',
'r':'r',
'phi0':'custom1',
'm6':'custom2',
'nt':'n_t',
'ombh2':'omega_b',
'omch2':'omega_cdm',
'omnuh2':'omega_ncdm',
'tau':'tau_reio',
'H0':'H0',
'massive_neutrinos':'N_ncdm',
'massless_neutrinos':'N_ur',
'Yp':'YHe',
'pivot_scalar':'k_pivot',
}
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
def __call__(self,
ombh2,
omch2,
H0,
As,
ns,
phi0,
m6,
tau,
w=None,
r=None,
nrun=None,
omk=0,
Yp=None,
Tcmb=2.7255,
massless_neutrinos=3.046,
l_max_scalar=3000,
l_max_tensor=3000,
pivot_scalar=0.05,
outputs=[],
**kwargs):
d={self.name_mapping[k]:v for k,v in locals().items()
if k in self.name_mapping and v is not None}
d['P_k_ini type']='external_Pk'
d['modes'] = 's,t'
self.model.set(output='tCl, lCl, pCl',
lensing='yes',
l_max_scalars=l_max_scalar,
command = '../LSODAtesnors/pk',
**d)
self.model.compute()
ell = arange(l_max_scalar+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max_scalar)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
class tsz_gal_cl:
def __init__(self):
# print 'Class for tSZ Cl'
# self.ptilde = np.loadtxt(LIBDIR+'/aux_files/ptilde.txt')
self.fort_lib_cl = cdll.LoadLibrary(LIBDIR + "/source/calc_cl")
self.fort_lib_cl.calc_cl_.argtypes = [
POINTER(c_double), #h0
POINTER(c_double), #obh2
POINTER(c_double), #och2
POINTER(c_double), #mnu
POINTER(c_double), #bias
POINTER(c_double), #Mcut
POINTER(c_double), #M1
POINTER(c_double), #kappa
POINTER(c_double), #sigma_Ncen
POINTER(c_double), #alp_Nsat
POINTER(c_double), #rmax
POINTER(c_double), #rgs
POINTER(c_int64), #pk_nk
POINTER(c_int64), #pk_nz
np.ctypeslib.ndpointer(dtype=np.double), #karr
np.ctypeslib.ndpointer(dtype=np.double), #pkarr
np.ctypeslib.ndpointer(dtype=np.double), #dndz
POINTER(c_int64), #nz_dndz
POINTER(c_double), #z1
POINTER(c_double), #z2
POINTER(c_double), #z1_ng
POINTER(c_double), #z2_ng
POINTER(c_int64), #nl
np.ctypeslib.ndpointer(dtype=np.double), #ell
np.ctypeslib.ndpointer(dtype=np.double), #gg
np.ctypeslib.ndpointer(dtype=np.double), #gy
np.ctypeslib.ndpointer(dtype=np.double), #tll
POINTER(c_double), #ng(z1<z<z2)
POINTER(c_int64), #flag_nu
POINTER(c_int64), #flag_tll
POINTER(c_int64), #nm
POINTER(c_int64) #nz
]
self.fort_lib_cl.calc_cl_.restype = c_void_p
# Calcualtion setup
self.kmin = 1e-3
self.kmax = 5.
self.zmax = 4. # should be consistent with fortran code
self.nk_pk = 200
self.nz_pk = 51
# Class
self.cosmo = Class()
def get_tsz_cl(self, ell_arr, params, dndz, z1, z2, z1_ng, z2_ng, nm, nz):
self.zmin = z1
self.zmax = z2
obh2 = params['obh2']
och2 = params['och2']
As = params['As']
ns = params['ns']
mnu = params['mnu']
mass_bias = params['mass_bias']
Mcut = params['Mcut']
M1 = params['M1']
kappa = params['kappa']
sigma_Ncen = params['sigma_Ncen']
alp_Nsat = params['alp_Nsat']
rmax = params['rmax']
rgs = params['rgs']
flag_nu_logic = params['flag_nu']
flag_tll_logic = params['flag_tll']
if type(flag_nu_logic) != bool:
print 'flag_nu must be boolean.'
sys.exit()
if flag_nu_logic:
flag_nu = 1
else:
flag_nu = 0
if type(flag_tll_logic) != bool:
print 'flag_tll must be boolean.'
sys.exit()
if flag_tll_logic:
flag_tll = 1
else:
flag_tll = 0
if 'theta' in params.keys():
theta = params['theta']
pars = {'output':'mPk','100*theta_s':theta,
'omega_b':obh2,'omega_cdm':och2,
'A_s':As,'n_s':ns,\
'N_ur':0.00641,'N_ncdm':1,'m_ncdm':mnu/3.,\
'T_ncdm':0.71611,\
'P_k_max_h/Mpc': self.kmax,'z_max_pk':self.zmax,\
'deg_ncdm':3.}
self.cosmo.set(pars)
self.cosmo.compute()
h0 = self.cosmo.h()
elif 'h0' in params.keys():
h0 = params['h0']
pars = {'output':'mPk','h':h0,
'omega_b':obh2,'omega_cdm':och2,
'A_s':As,'n_s':ns,\
'N_ur':0.00641,'N_ncdm':1,'m_ncdm':mnu/3.,\
'T_ncdm':0.71611,\
'P_k_max_h/Mpc': self.kmax,'z_max_pk':self.zmax,\
'deg_ncdm':3.}
self.cosmo.set(pars)
self.cosmo.compute()
derived = self.cosmo.get_current_derived_parameters(
['100*theta_s', 'sigma8'])
vz = (self.cosmo.angular_distance(z2_ng)**3*(1+z2_ng)**3 \
-self.cosmo.angular_distance(z1_ng)**3*(1+z1_ng)**3)
vz = vz * h0**3 * 4. * np.pi / 3.
derived['vz'] = vz
# get matter power spectra
kh_arr = np.logspace(np.log10(self.kmin), np.log10(self.kmax),
self.nk_pk)
kh = np.zeros((self.nz_pk, self.nk_pk))
pk = np.zeros((self.nz_pk, self.nk_pk))
pk_zarr = np.linspace(self.zmin, self.zmax, self.nz_pk)
for i in range(self.nz_pk):
kh[i, :] = kh_arr
if flag_nu == 0:
pk[i, :] = np.array([
self.cosmo.pk(k * h0, pk_zarr[i]) * h0**3 for k in kh_arr
])
elif flag_nu == 1:
pk[i, :] = np.array([
self.cosmo.pk_cb(k * h0, pk_zarr[i]) * h0**3
for k in kh_arr
])
# params
h0_in = byref(c_double(h0))
obh2_in = byref(c_double(obh2))
och2_in = byref(c_double(och2))
mnu_in = byref(c_double(mnu))
mass_bias_in = byref(c_double(mass_bias))
Mcut_in = byref(c_double(Mcut))
M1_in = byref(c_double(M1))
kappa_in = byref(c_double(kappa))
sigma_Ncen_in = byref(c_double(sigma_Ncen))
alp_Nsat_in = byref(c_double(alp_Nsat))
rmax_in = byref(c_double(rmax))
rgs_in = byref(c_double(rgs))
flag_nu_in = byref(c_int64(flag_nu))
flag_tll_in = byref(c_int64(flag_tll))
# dNdz
nz_dndz = byref(c_int64(len(dndz)))
# integration setting
z1_in = byref(c_double(self.zmin))
z2_in = byref(c_double(self.zmax))
# outputs
nl = len(ell_arr)
cl_gg = np.zeros((2, nl))
cl_gy = np.zeros((2, nl))
tll = np.zeros((nl * 2, nl * 2))
ng = c_double(0.0)
nl = c_int64(nl)
self.fort_lib_cl.calc_cl_(
h0_in, obh2_in, och2_in, mnu_in,\
mass_bias_in, \
Mcut_in, M1_in, kappa_in, sigma_Ncen_in, alp_Nsat_in,\
rmax_in, rgs_in,\
byref(c_int64(self.nk_pk)), byref(c_int64(self.nz_pk)),\
np.array(kh),np.array(pk),\
np.array(dndz),nz_dndz,\
z1_in, z2_in,\
byref(c_double(z1_ng)),byref(c_double(z2_ng)),\
nl,np.array(ell_arr),\
cl_gg,cl_gy,tll,ng,\
flag_nu_in,flag_tll_in,\
c_int64(nm), c_int64(nz)
)
self.cosmo.struct_cleanup()
return cl_gg, cl_gy, tll, ng.value, derived
kmsMpc = 3.33564095198145e-6
rd = cosmo.rs_drag()
print('rd=', rd)
for j in range(len(chunk)):
z = zs[j]
b1 = b1ar[j]
b2 = b2ar[j]
bG2 = bG2ar[j]
css0 = css0ar[j]
css2 = css2ar[j]
Pshot = Pshotar[j]
bGamma3 = bGamma3ar[j]
b4 = b4ar[j]
fz = cosmo.scale_independent_growth_factor_f(z)
da = cosmo.angular_distance(z)
# print('DA=',da)
hz = cosmo.Hubble(z) / kmsMpc
# print('Hz=',hz)
DV = (z * (1 + z)**2 * da**2 / cosmo.Hubble(z))**(1. / 3.)
# print('DV=',DV)
fs8 = cosmo.scale_independent_growth_factor(
z) * cosmo.scale_independent_growth_factor_f(z) * cosmo.sigma8()
# print('fs8=',fs8)
# print('sigma8=',cosmo.sigma8())
print('rd/DV=', rd / DV)
print('rdH=', rd * cosmo.Hubble(z))
print('rd/DA=', rd / da)
P2noW = np.zeros(k_size)
P0noW = np.zeros(k_size)
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill
"""
#{cosmoslik name : class name}
name_mapping = {'As':'A_s',
'ns':'n_s',
'r':'r',
'nt':'n_t',
'ombh2':'omega_b',
'omch2':'omega_cdm',
'omnuh2':'omega_ncdm',
'tau':'tau_reio',
'H0':'H0',
'massive_neutrinos':'N_ncdm',
'massless_neutrinos':'N_ur',
'Yp':'YHe',
'pivot_scalar':'k_pivot'}
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
def __call__(self,
ombh2,
omch2,
H0,
As,
ns,
tau,
omnuh2, #0.006
w=None,
r=None,
nrun=None,
omk=0,
Yp=None,
Tcmb=2.7255,
massive_neutrinos=1,
massless_neutrinos=2.046,
l_max_scalar=3000,
l_max_tensor=3000,
pivot_scalar=0.002,
outputs=[],
**kwargs):
self.model.set(output='tCl, lCl, pCl',
lensing='yes',
l_max_scalars=l_max_scalar,
**{self.name_mapping[k]:v for k,v in locals().items()
if k in self.name_mapping and v is not None})
self.model.compute()
ell = arange(l_max_scalar+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max_scalar)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
class classy(SlikPlugin):
"""
Plugin for CLASS.
Credit: Brent Follin, Teresa Hamill, Andy Scacco
"""
#{cosmoslik name : class name} - This needs to be done even for variables with the same name (because of for loop in self.model.set)!
name_mapping = {'As':'A_s',
'ns':'n_s',
'r':'r',
'k_c':'k_c',
'alpha_exp':'alpha_exp',
'nt':'n_t',
'ombh2':'omega_b',
'omch2':'omega_cdm',
'omnuh2':'omega_ncdm',
'tau':'tau_reio',
'H0':'H0',
'massive_neutrinos':'N_ncdm',
'massless_neutrinos':'N_ur',
'Yp':'YHe',
'pivot_scalar':'k_pivot',
#'Tcmb':'T_cmb',
#'P_k_max_hinvMpc':'P_k_max_h/Mpc'
#'w':'w0_fld',
#'nrun':'alpha_s',
#'omk':'Omega_k',
#'l_max_scalar':'l_max_scalars',
#'l_max_tensor':'l_max_tensors'
}
def __init__(self):
super(classy,self).__init__()
try:
from classy import Class
except ImportError:
raise Exception("Failed to import CLASS python wrapper 'Classy'.")
self.model = Class()
def __call__(self,
ombh2,
omch2,
H0,
As,
ns,
k_c,
alpha_exp,
tau,
#omnuh2=0, #0.006 #None means that Class will take the default for this, maybe?
w=None,
r=None,
nrun=None,
omk=0,
Yp=None,
Tcmb=2.7255,
#massive_neutrinos=0,
massless_neutrinos=3.046,
l_max_scalar=3000,
l_max_tensor=3000,
pivot_scalar=0.05,
outputs=[],
**kwargs):
self.model.set(output='tCl, lCl, pCl',
lensing='yes',
l_max_scalars=l_max_scalar,
**{self.name_mapping[k]:v for k,v in locals().items()
if k in self.name_mapping and v is not None})
self.model.compute()
ell = arange(l_max_scalar+1)
self.cmb_result = {'cl_%s'%x:(self.model.lensed_cl(l_max_scalar)[x.lower()])*Tcmb**2*1e12*ell*(ell+1)/2/pi
for x in ['TT','TE','EE','BB','PP','TP']}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {'H':self.model.Hubble(z),
'D_A':self.model.angular_distance(z),
'c':1.0,
'r_d':(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']}
class classy(SlikPlugin):
"""
Compute the CMB power spectrum with CLASS.
Based on work by: Brent Follin, Teresa Hamill
"""
#{cosmoslik name : class name}
name_mapping = {
'As': 'A_s',
'lmax': 'l_max_scalars',
'mnu': 'm_ncdm',
'Neff': 'N_ncdm',
'ns': 'n_s',
'nt': 'n_t',
'ombh2': 'omega_b',
'omch2': 'omega_cdm',
'omk': 'Omega_k',
'pivot_scalar': 'k_pivot',
'r': 'r',
'tau': 'tau_reio',
'Tcmb': 'T_cmb',
'Yp': 'YHe',
}
def __init__(self, **defaults):
super().__init__()
from classy import Class
self.model = Class()
self.defaults = defaults
def convert_params(self, **params):
"""
Convert from CosmoSlik params to CLASS
"""
params = {self.name_mapping.get(k, k): v for k, v in params.items()}
if 'theta' in params:
params['100*theta_s'] = 100 * params.pop('theta')
params['lensing'] = 'yes' if params.pop('DoLensing', True) else 'no'
return params
def __call__(self,
As=None,
DoLensing=True,
H0=None,
lmax=None,
mnu=None,
Neff=None,
nrun=None,
ns=None,
ombh2=None,
omch2=None,
omk=None,
output='tCl, lCl, pCl',
pivot_scalar=None,
r=None,
tau=None,
Tcmb=2.7255,
theta=None,
w=None,
Yp=None,
nowarn=False,
**kwargs):
if not nowarn and kwargs:
print('Warning: passing unknown parameters to CLASS: ' +
str(kwargs) + ' (set nowarn=True to turn off this message.)')
params = dict(
self.defaults, **{
k: v
for k, v in arguments(include_kwargs=False,
exclude=["nowarn"]).items()
if v is not None
})
self.model.set(self.convert_params(**params))
self.model.compute()
lmax = params['lmax']
ell = arange(lmax + 1)
if params['DoLensing'] == True:
self.cmb_result = {
x: (self.model.lensed_cl(lmax)[x.lower()]) * Tcmb**2 * 1e12 *
ell * (ell + 1) / 2 / pi
for x in ['TT', 'TE', 'EE', 'BB', 'PP', 'TP']
}
else:
self.cmb_result = {
x: (self.model.raw_cl(lmax)[x.lower()]) * Tcmb**2 * 1e12 *
ell * (ell + 1) / 2 / pi
for x in ['TT']
}
self.model.struct_cleanup()
self.model.empty()
return self.cmb_result
def get_bao_observables(self, z):
return {
'H':
self.model.Hubble(z),
'D_A':
self.model.angular_distance(z),
'c':
1.0,
'r_d':
(self.model.get_current_derived_parameters(['rs_rec']))['rs_rec']
}