def run_MCMC_ex(which_par,niter,save_title, renorm_var,firstiter=0,seed="none",guess="random"):
"""
Functions that runs the MCMC for a given set of parameters
Keyword Arguments:
which_par -- a list of indices, corresponding to the order defined above, exemple [0,2] means ombh2,tau if order is [ombh2,omch2,tau,As,ns,H0]
niter -- number of iterations in MCMC
renorm_var -- factor multipling the variance, to play around for better acceptance rate.
"""
cov_new_temp = cov_new[which_par,:][:,which_par] * renorm_var
string_temp = strings[which_par]
titles_temp = titles[which_par]
x_mean_temp = x_mean[which_par]
priors_central_temp = priors_central[which_par]
priors_invvar_temp = priors_invvar[which_par]
print titles_temp
sys.stdout.flush()
dd2 = cb.update_dic(dd,x_mean_temp,string_temp)
cl = cb.generate_spectrum(dd2)[:lmax+1,1]
cl[:2] = 1.e-35
if guess=="random":
guess_param = PS2P.prop_dist_form_params(x_mean_temp,cov_new_temp)
else :
guess_param = guess
tt1 = time.time()
save_string = "outputs/chain_ex_%s_%s_%d_%d"%(save_title,str(which_par).replace(',','').replace('[','').replace(']','').replace(' ',''),np.random.randint(0,100000),niter)
print save_string
sys.stdout.flush()
testss = np.array(MH.MCMC_log(guess_param, JJi.functional_form_params_n,PS2P.prop_dist_form_params, PS2P.prop_func_form_params,niter,PS2P.Gaussian_priors_func,firstiter,seed,save_string,[dlm,string_temp,dd,nl,bl,cl],[x_mean_temp*0,np.matrix(cov_new_temp)],[priors_central_temp,priors_invvar_temp]))
print time.time() - tt1
np.save(save_string+".npy",testss)
return testss
def run_MCMC(which_par,niter,save_title, renorm_var):
"""
Functions that runs the MCMC for a given set of parameters
Keyword Arguments:
which_par -- a list of indices, corresponding to the order defined above, exemple [0,2] means ombh2,tau if order is [ombh2,omch2,tau,As,ns,H0]
niter -- number of iterations in MCMC
renorm_var -- factor multipling the variance, to play around for better acceptance rate.
"""
cov_new_temp = cov_new[which_par,:][:,which_par] * renorm_var
string_temp = strings[which_par]
titles_temp = titles[which_par]
x_mean_temp = x_mean[which_par]
priors_central_temp = priors_central[which_par]
priors_invvar_temp = priors_invvar[which_par]
print titles_temp
# generate first guess parameters
guess_param = PS2P.prop_dist_form_params(x_mean_temp,cov_new_temp)
# generate first fluctuation map
dd2 = cb.update_dic(dd,guess_param,string_temp)
cl = cb.generate_spectrum(dd2)[:,1]
cl[:2] = 1.e-35
renorm = CG.renorm_term(cl,bl,nl)
fluc = hp.almxfl(CG.generate_w1term(cl[:lmax+1],bl[:lmax+1],nl[:lmax+1]) + CG.generate_w0term(cl[:lmax+1]),renorm)
# the core of the MCMC
testss = np.array(MH.MCMC_log_Jeff_test(guess_param, JJi.target_distrib_newrescale,PS2P.prop_dist_form_params, PS2P.prop_func_form_params,niter,PS2P.Gaussian_priors_func,[[dlm,string_temp,dd,nl[:lmax+1],bl[:lmax+1]],[cl[:lmax+1],fluc]],[x_mean_temp*0,np.matrix(cov_new_temp)],[priors_central_temp,priors_invvar_temp]))
np.save("chain_%s_%s_%d_%d.npy"%(save_title,str(which_par).replace(',','').replace('[','').replace(']','').replace(' ',''),np.random.randint(0,100000),niter),testss)
return testss
def test_3rd_Term(list_guess):
#initial guess
guess_param = PS2P.prop_dist_form_params(x_mean,cov_new)
# generate first fluctuation map
dd2 = cb.update_dic(dd,guess_param,strings)
cl = cb.generate_spectrum(dd2)[:,1]
cl[:2] = 1.e-35
renorm = CG.renorm_term(cl,bl,nl)
fluc = hp.almxfl(CG.generate_w1term(cl[:lmax+1],bl[:lmax+1],nl[:lmax+1]) + CG.generate_w0term(cl[:lmax+1]),renorm)
Cl_i,fluc_i = [cl,fluc]
list_save=[]
for i in range(len(list_guess)):
print i
test_param = list_guess[i]
Like_3,Cl_ip1,fluc_ip1,fluc_i_rescaled = test_fluctuation_term(test_param,[dlm,strings,dd2,nl[:lmax+1],bl[:lmax+1]],[Cl_i, fluc_i])
Cl_i,fluc_i = [Cl_ip1,fluc_ip1]
list_save.append([Like_3,Cl_ip1,fluc_ip1,fluc_i_rescaled])
return list_save
def run_MCMC(which_par,niter,save_title, renorm_var):
"""
Functions that runs the MCMC for a given set of parameters
Keyword Arguments:
which_par -- a list of indices, corresponding to the order defined above, exemple [0,2] means ombh2,tau if order is [ombh2,omch2,tau,As,ns,H0]
niter -- number of iterations in MCMC
renorm_var -- factor multipling the variance, to play around for better acceptance rate.
"""
cov_new_temp = cov_new[which_par,:][:,which_par] * renorm_var
string_temp = strings[which_par]
titles_temp = titles[which_par]
x_mean_temp = x_mean[which_par]
priors_central_temp = priors_central[which_par]
priors_invvar_temp = priors_invvar[which_par]
print titles_temp
guess_param = PS2P.prop_dist_form_params(x_mean_temp,cov_new_temp)
testss = np.array(MH.MCMC_log(guess_param, PS2P.functional_form_params_n,PS2P.prop_dist_form_params, PS2P.prop_func_form_params,niter,PS2P.Gaussian_priors_func,[dlm,string_temp,dd,nl,bl],[x_mean_temp*0,np.matrix(cov_new_temp)],[priors_central_temp,priors_invvar_temp]))
#print "%.2f rejected; %.2f accepted; %.2f Lucky accepted"%((flag==0).mean(),(flag==1).mean(),(flag==2).mean())
np.save("chain_%s_%s_%d_%d.npy"%(save_title,str(which_par).replace(',','').replace('[','').replace(']','').replace(' ',''),np.random.randint(0,100000),niter),testss)
return testss
