sys.path.insert(0, '/Users/hazboun/software_development/pta_sim/')
import pta_sim
import pta_sim.parse_sim as parse_sim
from pta_sim.sim_gw import Simulation, model_simple
from pta_sim.bayes import chain_length_bool, save_core, get_freqs, filter_psr_path
args = parse_sim.arguments()
#Is chain longer than niter?
longer = chain_length_bool(args.outdir, args.niter)
if longer and os.path.exists(args.core_path):
sys.end()
elif longer:
save_core(args.corepath, args.outdir)
sys.end() #Hmmmm what to do here?
else:
pass
#Get par and tim files.
parfiles = sorted(glob.glob(args.pardir + '*.par'))
timfiles = sorted(glob.glob(args.timdir + '*.tim'))
if args.psr_list is not None:
parfiles = filter_psr_path(parfiles, args.psr_list, rhs='_')
timfiles = filter_psr_path(timfiles, args.psr_list, rhs='_')
sim = Simulation(parfiles, timfiles, ephem=args.ephem, verbose=True)
if args.process is None:
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,
burn=100000)
save_core(args.corepath, args.outdir, remove=True)