def get_All_results():
# Modeling each pulsar.
tm = gp_signals.TimingModel()
wnb = models.white_noise_block(vary=True)
dmn = models.dm_noise_block(components=30)
spn = models.red_noise_block(components=30)
model = tm + wnb + dmn + spn
ptas = [signal_base.PTA(model(psr)) for psr in psrs]
# Multiprocessing.
jobs = []
RETs={}
for i in range(len(psrs)):
RETs[i] = multiprocessing.Manager().Value('i',0)
p = multiprocessing.Process(target=get_pulsar_noise, args=(ptas[i],RETs[i]))
jobs.append(p)
p.start()
for p in jobs:
p.join()
# Return the sum of the Ln Maximal Likelihood values.
MLHselect = [RET.value for RET in RETs.values()]
return MLHselect
def test_model_2a_altpol_spectrum(nodmx_psrs, caplog):
log10_A_tt = parameter.LinearExp(-18, -12)('log10_A_tt')
log10_A_st = parameter.LinearExp(-18, -12)('log10_A_st')
log10_A_vl = parameter.LinearExp(-18, -15)('log10_A_vl')
log10_A_sl = parameter.LinearExp(-18, -16)('log10_A_sl')
kappa = parameter.Uniform(0, 15)('kappa')
p_dist = parameter.Normal(1.0, 0.2)
pl = model_orfs.generalized_gwpol_psd(log10_A_tt=log10_A_tt,
log10_A_st=log10_A_st,
log10_A_vl=log10_A_vl,
log10_A_sl=log10_A_sl,
kappa=kappa,
p_dist=p_dist,
alpha_tt=-2 / 3,
alpha_alt=-1)
s = models.white_noise_block(vary=False, inc_ecorr=True)
s += models.red_noise_block(prior='log-uniform')
s += gp_signals.FourierBasisGP(spectrum=pl, name='gw')
s += gp_signals.TimingModel()
m = signal_base.PTA([s(psr) for psr in nodmx_psrs])
m.set_default_params(noise_dict)
for param in m.params:
if 'gw_p_dist' in str(param):
# get pulsar name and distance
psr_name = str(param).split('_')[0].strip('"')
psr_dist = [p._pdist for p in nodmx_psrs if psr_name in p.name][0]
# edit prior settings
param._prior = parameter.Normal(mu=psr_dist[0], sigma=psr_dist[1])
param._mu = psr_dist[0]
param._sigma = psr_dist[1]
assert hasattr(m, 'get_lnlikelihood')
# spin noise
log10_A = parameter.Constant()
gamma = parameter.Constant()
pl = utils.powerlaw(log10_A=log10_A, gamma=gamma)
selection = selections.Selection(selections.no_selection)
spn = gp_signals.FourierBasisGP(pl,
components=50,
Tspan=None,
coefficients=False,
selection=selection,
modes=None)
dp = DPDM.dpdm_block(type_='Bayes')
tm = gp_signals.TimingModel()
eph = deterministic_signals.PhysicalEphemerisSignal(use_epoch_toas=True)
wnb = models.white_noise_block(vary=False)
model = tm + dp + wnb + dmn + spn + eph
nparams = [] # Get the number of parameters of each pulsar
signals = []
for psr in psrs:
signal = model(psr)
nparams.append(len(signal.params) -
5) # Subtracting common DPDM params
signals.append(signal)
PTA = signal_base.PTA(signals)
ndim = len(PTA.params) + 5
# Use the best fit noise parameters!
PTA.set_default_params(Dict)
noise = {}
for p in psrs:
noise.update(noise_json[p.name])
if args.tspan is None:
Tspan = model_utils.get_tspan(psrs)
else:
Tspan = args.tspan
if args.wideband:
inc_ecorr = False
else:
inc_ecorr = True
### White Noise ###
wn = models.white_noise_block(vary=False, inc_ecorr=inc_ecorr)
### Red Noise ###
rn_plaw = models.red_noise_block(psd='powerlaw',
prior='log-uniform',
Tspan=Tspan,
components=30,
gamma_val=None)
### GWB ###
gw = models.common_red_noise_block(psd='powerlaw',
prior='log-uniform',
Tspan=Tspan,
gamma_val=13 / 3.,
name='gw')
base_model = wn
if args.bayes_ephem:
elif longer:
c0 = Core(chaindir=Outdir)
c0.set_rn_freqs(freq_path=Outdir+'/achrom_freqs.txt')
c0.save(args.corepath+f'{psr.name}.core')
sys.exit() #Hmmmm what to do here?
else:
pass
emp_dist_path = args.emp_distr
if args.gwb_bf or args.gwb_ul:
if args.gwb_bf:
prior = 'log-uniform'
elif args.gwb_ul:
prior = 'uniform'
m = models.white_noise_block(vary=True, inc_ecorr=True)
m += gp_signals.TimingModel(use_svd=False)
m += models.red_noise_block(psd=args.psd, prior=prior,
components=args.nfreqs, gamma_val=None)
m += models.common_red_noise_block(gamma_val=13/3., prior=prior,
psd=args.psd, components=args.nfreqs)
pta = signal_base.PTA(m(psr))
else:
if args.gfl:
vary_rn = False
else:
vary_rn = True
pta = models.model_singlepsr_noise(psr, red_var=vary_rn,
psd=args.psd, Tspan=args.tspan,
components=args.nfreqs,
datadir = '../ppta/fit1data'
parfiles = sorted(glob.glob(datadir + '/*.par'))
timfiles = sorted(glob.glob(datadir + '/*.tim'))
parfiles.remove(datadir + '/J1125-6014.par')
timfiles.remove(datadir + '/J1125-6014.tim')
psrs = []
for p, t in zip(parfiles, timfiles):
psr = Pulsar(p, t)
psrs.append(psr)
# Modeling the signals
dp = DPDM.dpdm_block(type_='Bayes')
tm = gp_signals.TimingModel()
wnb = models.white_noise_block(vary=True)
dmn = models.dm_noise_block(components=50)
spn = models.red_noise_block(components=50)
model = tm + dp + wnb + dmn + spn
nparams = [] # Get the number of parameters of each pulsar
signals = []
for psr in psrs:
signal = model(psr)
nparams.append(len(signal.params) -
5) # Subtracting common DPDM params
signals.append(signal)
PTA = signal_base.PTA(signals)
ndim = len(PTA.params)
# Get Starting Points and constant parameter values.
save1 = np.load('noisepars.npy')
