q[self.pmap[str(param)]] = np.random.uniform(param.prior._defaults['pmin'],
param.prior._defaults['pmax'])
# forward-backward jump probability
lqxy = (param.get_logpdf(x[self.pmap[str(param)]]) -
param.get_logpdf(q[self.pmap[str(param)]]))
return q, float(lqxy)
sampler.JumpProposal.draw_from_gw_gamma_prior = draw_from_gw_gamma_prior
Sampler.addProposalToCycle(Sampler.jp.draw_from_gw_gamma_prior, 25)
try:
achrom_freqs = get_freqs(pta_crn, signal_id='gw_crn')
np.savetxt(args.outdir + 'achrom_freqs.txt', achrom_freqs, fmt='%.18e')
except:
pass
x0 = np.hstack(p.sample() for p in pta_crn.params)
Sampler.sample(x0,
args.niter,
SCAMweight=200,
AMweight=100,
DEweight=200,
burn=50000,
writeHotChains=args.writeHotChains,
hotChain=args.hot_chain,
Tskip=100,
Tmax=args.tempmax)
ptas = {0:pta_std,
1:pta_low}
pta_std.set_default_params(noise)
pta_low.set_default_params(noise)
with open(args.pta_pkl,'wb') as fout:
cloudpickle.dump(ptas,fout)
hm = hypermodel.HyperModel(models=ptas)
sampler = hm.setup_sampler(outdir=args.outdir, resume=True,
empirical_distr = args.emp_distr)
try:
gw_std_freqs = get_freqs(pta_std, signal_id='gw')
gw_low_freqs = get_freqs(pta_low, signal_id='gw')
np.savetxt(args.outdir + 'low_gw_freqs.txt', gw_low_freqs, fmt='%.18e')
np.savetxt(args.outdir + 'std_gw_freqs.txt', gw_std_freqs, fmt='%.18e')
except:
pass
model_params = {}
for ii,mod in enumerate(ptas):
model_params.update({ii : ptas[ii].param_names})
with open(args.outdir+'/model_params.json' , 'w') as fout:
json.dump(model_params, fout, sort_keys=True, indent=4,
separators=(',', ': '))
x0 = hm.initial_sample()
# initial jump covariance matrix
cov = np.diag(np.ones(ndim) * 0.01**2)
# set up jump groups by red noise groups
groups = model_utils.get_parameter_groups(pta)
sampler = ptmcmc(ndim,
pta.get_lnlikelihood,
pta.get_lnprior,
cov,
groups=groups,
outDir=args.outdir,
resume=True)
achrom_freqs = get_freqs(pta)
np.save(args.outdir + 'pars.npy', pta.param_names)
np.save(args.outdir + 'par_model.npy', np.array(pta.params).astype(str))
np.save(args.outdir + 'signals.npy', list(pta.signals.keys()))
np.savetxt(args.outdir + 'achrom_rn_freqs.txt', achrom_freqs, fmt='%.18e')
jp = model_utils.JumpProposal(pta)
sampler.addProposalToCycle(jp.draw_from_prior, 15)
sampler.addProposalToCycle(jp.draw_from_red_prior, 15)
sampler.addProposalToCycle(jp.draw_from_gwb_log_uniform_distribution, 15)
if args.bayes_ephem:
sampler.addProposalToCycle(jp.draw_from_ephem_prior, 15)
N = int(args.niter)
sampler.sample(x0,
model_1 = base_model + rn_plaw
model_2a = model_1 + gw
pta_noise = signal_base.PTA([model_1(p) for p in psrs])
pta_noise.set_default_params(noise)
pta_gw = signal_base.PTA([model_2a(p) for p in psrs])
pta_gw.set_default_params(noise)
ptas = {0: pta_noise, 1: pta_gw}
hm = model_utils.HyperModel(models=ptas)
sampler = hm.setup_sampler(outdir=args.outdir, resume=True)
achrom_freqs = get_freqs(ptas[0])
# np.save(args.outdir + 'pars.npy', pta.param_names)
# np.save(args.outdir + 'par_model.npy', np.array(pta.params).astype(str))
# np.save(args.outdir + 'signals.npy', list(pta.signals.keys()))
np.savetxt(args.outdir + 'achrom_rn_freqs.txt', achrom_freqs, fmt='%.18e')
x0 = hm.initial_sample()
sampler.sample(
x0,
args.niter,
SCAMweight=30,
AMweight=15,
DEweight=50,
)
save_core(args.corepath, args.outdir)
cov = np.diag(np.ones(ndim) * 0.01**2)
# set up jump groups by red noise groups
groups = sampler.get_parameter_groups(pta)
Sampler = ptmcmc(ndim, pta.get_lnlikelihood, pta.get_lnprior,
cov, groups=groups, outDir=args.outdir, resume=True)
np.save(args.outdir + 'pars.npy', pta.param_names)
np.save(args.outdir + 'par_model.npy', np.array(pta.params).astype(str))
np.save(args.outdir + 'signals.npy', list(pta.signals.keys()))
try:
achrom_freqs = get_freqs(pta, signal_id='gw')
np.savetxt(args.outdir + 'achrom_rn_freqs.txt', achrom_freqs, fmt='%.18e')
except:
pass
jp = sampler.JumpProposal(pta)
Sampler.addProposalToCycle(jp.draw_from_prior, 15)
if args.rednoise:
Sampler.addProposalToCycle(jp.draw_from_red_prior, 15)
Sampler.addProposalToCycle(jp.draw_from_gwb_log_uniform_distribution, 15)
if args.bayes_ephem:
Sampler.addProposalToCycle(jp.draw_from_ephem_prior, 15)
N = int(args.niter)
Sampler.sample(x0, Niter=N, SCAMweight=30, AMweight=15,
groups.append([pta.param_names.index(p) for p in gp_pars])
# initial jump covariance matrix
cov = np.diag(np.ones(ndim) *
0.01**2) # helps to tune MCMC proposal distribution
# sampler object
sampler = ptmcmc(ndim,
pta.get_lnlikelihood,
pta.get_lnprior,
cov,
groups=groups,
outDir=args.outdir,
resume=True)
achrom_freqs = get_freqs(pta, 'red_noise')
np.save(args.outdir + 'pars.npy', pta.param_names)
np.save(args.outdir + 'par_model.npy', np.array(pta.params).astype(str))
np.save(args.outdir + 'signals.npy', list(pta.signals.keys()))
np.savetxt(args.outdir + 'achrom_rn_freqs.txt', achrom_freqs, fmt='%.18e')
jp = model_utils.JumpProposal(pta)
sampler.addProposalToCycle(jp.draw_from_dm_gp_prior, 20)
sampler.addProposalToCycle(jp.draw_from_prior, 20)
# sampler.addProposalToCycle(jp.draw_from_red_prior, 20)
sampler.sample(x0,
args.niter,
SCAMweight=30,
AMweight=15,
DEweight=50,
vary_rn = True
pta = models.model_singlepsr_noise(psr, red_var=vary_rn,
psd=args.psd, Tspan=args.tspan,
components=args.nfreqs,
factorized_like=args.gfl,
gw_components=args.n_gwbfreqs,
fact_like_logmin=-14.2,
fact_like_logmax=-1.2,
is_wideband=args.wideband)
emp_dist_path = args.emp_distr.replace('PSR_NAME',psr.name)
Sampler = sampler.setup_sampler(pta=pta,
outdir=Outdir,
empirical_distr = emp_dist_path,
resume=True)
if args.gfl:
freqs = bys.get_freqs(pta, signal_id='gw')
else:
freqs = bys.get_freqs(pta, signal_id='red_noise')
np.savetxt(Outdir+'achrom_freqs.txt', freqs)
x0 = np.hstack(p.sample() for p in pta.params)
Sampler.sample(x0, Niter=args.niter, burn=100000,
writeHotChains=args.writeHotChains)
c0 = Core(chaindir=Outdir)
c0.set_rn_freqs(freq_path=Outdir+'/achrom_freqs.txt')
c0.save(args.corepath+f'{psr.name}.core')
dm_var=True,
dm_type='gp',
dm_psd='powerlaw',
dm_annual=False,
white_vary=False,
gequad=False,
dm_chrom=False,
dmchrom_psd='powerlaw',
dmchrom_idx=4,
red_select=None,
red_breakflat=False,
red_breakflat_fq=None,
coefficients=False,
)
else:
raise NotImplementedError('Please add this model to the script.')
Sampler = sampler.setup_sampler(pta=pta,
outdir=Outdir,
empirical_distr=args.emp_distr,
resume=True)
try:
freqs = bys.get_freqs(pta, signal_id='gw')
np.savetxt(Outdir + 'achrom_freqs.txt', freqs)
except:
pass
x0 = np.hstack(p.sample() for p in pta.params)
Sampler.sample(x0, Niter=args.niter)
