def valueCross(self,pars):
qso_boost = pars["qso_metal_boost"]
qso_evol = [pars['qso_evol_0'],pars['qso_evol_1']]
bias_qso = pars["bias_qso"]
growth_rate = pars["growth_rate"]
beta_qso = growth_rate/bias_qso
bias_met = sp.array([pars['bias_'+met] for met in self.met_names])
beta_met = sp.array([pars['beta_'+met] for met in self.met_names])
Lpar = pars["Lpar_cross"]
Lper = pars["Lper_cross"]
### Scales
if (self.different_drp):
drp_met = sp.array([pars['drp_'+met] for met in self.met_names])
drp = sp.outer(sp.ones(self.nd_cross),drp_met)
else:
drp = pars["drp"]
if self.grid:
### Redshift evolution
z = self.grid_qso_met[:,:,2]
evol = sp.power( self.evolution_growth_factor(z)/self.evolution_growth_factor(self.zref),2. )
evol *= self.evolution_Lya_bias(z,[pars["alpha_lya"]])/self.evolution_Lya_bias(self.zref,[pars["alpha_lya"]])
evol *= self.evolution_QSO_bias(z,qso_evol)/self.evolution_QSO_bias(self.zref,qso_evol)
rp_shift = self.grid_qso_met[:,:,0]+drp
rt = self.grid_qso_met[:,:,1]
r = sp.sqrt(rp_shift**2 + rt**2)
mur = rp_shift/r
muk = cosmo_model.muk
kp = self.k * muk
kt = self.k * sp.sqrt(1.-muk**2)
### Correction to linear power-spectrum
pk_corr = (1.+0.*muk)*self.pk
pk_corr *= sp.sinc(kp*Lpar/2./sp.pi)**2
pk_corr *= sp.sinc(kt*Lper/2./sp.pi)**2
### Biases
b1b2 = qso_boost*bias_qso*bias_met
if self.grid:
xi_qso_met = sp.zeros(self.grid_qso_met[:,0,0].size)
for i in range(self.nmet):
pk_full = b1b2[i]*(1. + beta_met[i]*muk**2)*(1. + beta_qso*muk**2)*pk_corr
xi_qso_met += cosmo_model.Pk2Xi(r[:,i],mur[:,i],self.k,pk_full,ell_max=self.ell_max)*evol[:,i]
else:
nbins = list(self.xdmat.values())[0].shape[0]
xi_qso_met = sp.zeros(nbins)
for i in self.met_names:
bias_met = pars["bias_"+i]
beta_met = pars["beta_"+i]
recalc = beta_met != self.prev_pmet['beta_'+i] or\
growth_rate != self.prev_pmet['growth_rate'] or\
not sp.allclose(qso_evol,self.prev_pmet['qso_evol']) or\
self.prev_pmet['drp'] != drp
if recalc:
if self.verbose:
print("recalculating metal {}".format(i))
self.prev_pmet['beta_'+i] = beta_met
self.prev_pmet['growth_rate'] = growth_rate
self.prev_pmet['qso_evol'] = qso_evol
self.prev_pmet['drp'] = drp
z = self.xzeff[i]
evol = sp.power( self.evolution_growth_factor(z)/self.evolution_growth_factor(self.zref),2. )
evol *= self.evolution_Lya_bias(z,[pars["alpha_"+i]])/self.evolution_Lya_bias(self.zref,[pars["alpha_"+i]])
evol *= self.evolution_QSO_bias(z,qso_evol)/self.evolution_QSO_bias(self.zref,qso_evol)
rp = self.xrp[i] + drp
rt = self.xrt[i]
r = sp.sqrt(rp**2+rt**2)
w=r==0
r[w]=1e-6
mur = rp/r
pk_full = (1. + beta_met*muk**2)*(1. + beta_qso*muk**2)*pk_corr
self.prev_xi_qso_met[i] = cosmo_model.Pk2Xi(r,mur,self.k,pk_full,ell_max=self.ell_max)
self.prev_xi_qso_met[i] = self.xdmat[i].dot(self.prev_xi_qso_met[i]*evol)
xi_qso_met += qso_boost*bias_qso*bias_met*self.prev_xi_qso_met[i]
return xi_qso_met
def valueAuto(self,pars):
bias_lya=pars["bias_lya*(1+beta_lya)"]
beta_lya=pars["beta_lya"]
bias_lya /= 1+beta_lya
if self.templates:
bias_met=sp.array([pars['bias_'+met] for met in self.met_names])
beta_met=sp.array([pars['beta_'+met] for met in self.met_names])
amp=sp.zeros([self.nmet,3])
amp[:,0] = bias_met*(1 + (beta_lya+beta_met)/3 + beta_lya*beta_met/5)
amp[:,1] = bias_met*(2*(beta_lya+beta_met)/3 + 4*beta_lya*beta_met/7)
amp[:,2] = bias_met*8*beta_met*beta_lya/35
amp*=bias_lya
xi_lya_met=amp*self.temp_lya_met
xi_lya_met=sp.sum(xi_lya_met,axis=(1,2))
amp=sp.zeros([self.nmet,self.nmet,3])
bias_met2 = bias_met*bias_met[None,:]
amp[:,:,0] = bias_met2*(1+(beta_met+beta_met[None,:])/3+beta_met*beta_met[None,:]/5)
amp[:,:,1] = bias_met2*(2*(beta_met+beta_met[None,:])/3+4*beta_met*beta_met[None,:]/7)
amp[:,:,2] = bias_met2*8*beta_met*beta_met[None,:]/35
xi_met_met=amp*self.temp_met_met
xi_met_met=sp.sum(xi_met_met,axis=(1,2,3))
else:
muk = cosmo_model.muk
k = self.k
kp = k*muk
kt = k*sp.sqrt(1-muk**2)
nbins = self.dmat["LYA_"+self.met_names[0]].shape[0]
if self.hcds_mets:
bias_lls = pars["bias_lls"]
beta_lls = pars["beta_lls"]
L0_lls = pars["L0_lls"]
Flls = sp.sin(kp*L0_lls)/(kp*L0_lls)
Lpar_auto = pars["Lpar_auto"]
Lper_auto = pars["Lper_auto"]
alpha_lya = pars["alpha_lya"]
Gpar = sp.sinc(kp*Lpar_auto/2/sp.pi)**2
Gper = sp.sinc(kt*Lper_auto/2/sp.pi)**2
xi_lya_met = sp.zeros(nbins)
for met in self.met_names:
bias_met = pars['bias_'+met]
beta_met = pars['beta_'+met]
alpha_met = pars["alpha_"+met]
dm = self.dmat["LYA_"+met]
recalc = beta_met != self.prev_pmet["beta_"+met]\
or beta_lya != self.prev_pmet["beta_lya"]\
or alpha_lya != self.prev_pmet["alpha_lya"]\
or alpha_met != self.prev_pmet["alpha_"+met]
rt = self.auto_rt["LYA_"+met]
rp = self.auto_rp["LYA_"+met]
zeff = self.auto_zeff["LYA_"+met]
r = sp.sqrt(rt**2+rp**2)
w = (r==0)
r[w] = 1e-6
mur = rp/r
if recalc:
if self.verbose:
print("recalculating ",met)
pk = (1+beta_lya*muk**2)*(1+beta_met*muk**2)*self.pk
pk *= Gpar*Gper
xi = cosmo_model.Pk2Xi(r,mur,self.k,pk,ell_max=self.ell_max)
xi *= ((1+zeff)/(1+self.zref))**((alpha_lya-1)*(alpha_met-1))
self.prev_xi_lya_met["LYA_"+met] = self.dmat["LYA_"+met].dot(xi)
if self.hcds_mets:
recalc = self.prev_pmet["beta_lls"] != beta_lls\
or self.prev_pmet["L0_lls"] != L0_lls
if recalc:
pk = (1+beta_lls*muk**2)*(1+beta_met*muk**2)*self.pk*Flls
pk *= Gpar*Gper
xi = cosmo_model.Pk2Xi(r,mur,self.k,pk,ell_max=self.ell_max)
xi *= ((1+zeff)/(1+self.zref))**((alpha_lya-1)*(alpha_met-1))
self.prev_xi_dla_met[met] = xi
xi_lya_met += bias_lya*bias_met*self.prev_xi_lya_met["LYA_"+met]
if self.hcds_mets:
xi_lya_met += bias_lls*bias_met*self.prev_xi_dla_met[met]
xi_met_met = sp.zeros(nbins)
for i,met1 in enumerate(self.met_names):
bias_met1 = pars['bias_'+met1]
beta_met1 = pars['beta_'+met1]
alpha_met1 = pars["alpha_"+met1]
for met2 in self.met_names[i:]:
rt = self.auto_rt[met1+"_"+met2]
rp = self.auto_rp[met1+"_"+met2]
zeff = self.auto_zeff[met1+"_"+met2]
bias_met2 = pars['bias_'+met2]
beta_met2 = pars['beta_'+met2]
alpha_met2 = pars["alpha_"+met2]
dm = self.dmat[met1+"_"+met2]
recalc = beta_met1 != self.prev_pmet["beta_"+met1]\
or beta_met2 != self.prev_pmet["beta_"+met2]
if recalc:
if self.verbose:
print("recalculating ",met1,met2)
r = sp.sqrt(rt**2+rp**2)
w=r==0
r[w]=1e-6
mur = rp/r
pk = (1+beta_met1*muk**2)*(1+beta_met2*muk**2)*self.pk
pk *= Gpar*Gper
xi = cosmo_model.Pk2Xi(r,mur,self.k,pk,ell_max=self.ell_max)
xi *= ((1+zeff)/(1+self.zref))**((alpha_met1-1)*(alpha_met2-1))
self.prev_xi_met_met[met1+"_"+met2] = self.dmat[met1+"_"+met2].dot(xi)
xi_met_met += bias_met1*bias_met2*self.prev_xi_met_met[met1+"_"+met2]
for i in self.prev_pmet:
self.prev_pmet[i] = pars[i]
return xi_lya_met + xi_met_met