def diff_fixed_like(fluc_lm_determ,fluc_lm_GS,mf_lm_new,mf_lm_old,Cl_new,Cl_old, *arg):
"""
Keyword Arguments:
x -- the vector of params
*args are:
arg[0] -- a target class object
arg[1] -- Cl_old, fluc_lm_old
"""
##print guess, arg
dlm,strings,params,nl,bl = arg[0]
tt1_new = -1/2.*np.real(np.vdot(CG.complex2real_alm(dlm-hp.almxfl(mf_lm_new,bl).T),CG.complex2real_alm(hp.almxfl((dlm-hp.almxfl(mf_lm_new,bl)),1/nl))))
tt1_old = -1/2.*np.real(np.vdot(CG.complex2real_alm(dlm-hp.almxfl(mf_lm_old,bl).T),CG.complex2real_alm(hp.almxfl((dlm-hp.almxfl(mf_lm_old,bl)),1/nl))))
#print "chi**2 = ",tt1_new-tt1_old
# "mean field part" of the likelihood
tt2_new = -1/2. *np.real(np.vdot(CG.complex2real_alm(mf_lm_new.T),CG.complex2real_alm(hp.almxfl((mf_lm_new),1./Cl_new))))
tt2_old = -1/2. *np.real(np.vdot(CG.complex2real_alm(mf_lm_old.T),CG.complex2real_alm(hp.almxfl((mf_lm_old),1./Cl_old))))
#print "'mf like' = ",tt2_new-tt2_old
# "fluctuation part" of the likelihood
tt3_new = -1/2. *np.real(np.vdot(CG.complex2real_alm(fluc_lm_determ.T),CG.complex2real_alm(hp.almxfl((fluc_lm_determ),1/nl*bl**2))))
tt3_old = -1/2. *np.real(np.vdot(CG.complex2real_alm(fluc_lm_GS.T),CG.complex2real_alm(hp.almxfl((fluc_lm_GS),1/nl*bl**2))))
#print "'fluc like' = ",tt3_new-tt3_old
tt_new = tt1_new + tt2_new + tt3_new
tt_old = tt1_old + tt2_old + tt3_old
return tt_new,tt_old
def functional_form_params_n(x,*arg):
"""
Keyword Arguments:
x -- the vector of params
*args are:
dlm -- input map
x_str -- the dictionary strings corresponding to x
params -- a camber dictionnary
noise -- a noise power spectrum
beam -- a beam power spectrum
"""
dlm = arg[0]
strings = arg[1]
params = arg[2].copy()
noise = arg[3]
beam = arg[4]
Cl_old = arg[5]
lmax = len(Cl_old)-1
#params["output_root"] = '../Codes/CG_git/MH_MCMC/camb_ini/test%d'%np.random.randint(100)
for i in range(np.size(x)):
##print strings[i]
if strings[i]=='scalar_amp(1)':
##print params[strings[i]]
params[strings[i]]=np.exp(x[i])*1e-10
##print params[strings[i]]
else:
params[strings[i]]=x[i]
Cl = cb.generate_spectrum(params)
#lmax = Cl.shape[0]-1
tt = -1./2 * np.real(np.vdot(CG.complex2real_alm(dlm.T),CG.complex2real_alm(hp.almxfl(dlm,1/(beam[:lmax+1]**2*Cl[:lmax+1,1]+noise[:lmax+1])))))
#determinant is the product of the diagonal element: in log:
tt2 = - 1./2 *((2*np.arange(2,lmax+1)+1)*np.log(noise[2:lmax+1]+Cl[2:lmax+1,1]*beam[2:lmax+1]**2)).sum()
return tt+tt2,Cl[:,1]
