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:
base_model += deterministic_signals.PhysicalEphemerisSignal(
use_epoch_toas=True)
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])
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,
factorized_like=args.gfl,
gw_components=args.n_gwbfreqs,
fact_like_logmin=-14.2,
fact_like_logmax=-1.2,
is_wideband=args.wideband)
dp_thresh = parameter.Uniform(0,1)('k_threshold')
else:
dp_thresh = args.dp_thresh
pl = dropout.dropout_powerlaw(log10_A=log10_A, gamma=gamma,
k_drop=k_drop, k_threshold=dp_thresh)
rn_plaw = gp_signals.FourierBasisGP(pl, components=30,
Tspan=Tspan, name='red_noise')
else:
rn_plaw = models.red_noise_block(psd='powerlaw', prior='log-uniform',
Tspan=Tspan, components=30,
gamma_val=None)
### GWB ###
crn = models.common_red_noise_block(psd='powerlaw', prior=prior,
components=args.n_gwbfreqs,
Tspan=Tspan, gamma_val=13/3., name='gw')
gw = models.common_red_noise_block(psd='powerlaw', prior=prior,
components=args.n_gwbfreqs, orf='hd',
Tspan=Tspan, gamma_val=13/3., name='gw')
base_model = tm + wn
if args.bayes_ephem:
base_model += deterministic_signals.PhysicalEphemerisSignal(use_epoch_toas=True)
if args.rn_psrs[0]=='all':
rn_psrs='all'
else:
rn_psrs=args.rn_psrs
model += gp_signals.TimingModel(use_svd=False)
model += models.red_noise_block(psd=args.psd,
prior=prior,
components=args.nfreqs,
gamma_val=None)
if args.gwb_off:
pass
else:
if args.hd:
orf = 'hd'
else:
orf = None
gw = models.common_red_noise_block(psd=args.psd,
prior=prior,
Tspan=Tspan,
orf=orf,
gamma_val=args.gamma_gw,
name='gw')
model += gw
log10_sigma = parameter.Uniform(-10, -4)
log10_ell = parameter.Uniform(1, 4)
dm_basis = linear_interp_basis_dm(dt=15 * 86400)
dm_prior = se_dm_kernel(log10_sigma=log10_sigma, log10_ell=log10_ell)
dm_gp = gp_signals.BasisGP(dm_prior, dm_basis, name='dm_gp')
dm_block = dm_gp
# Make solar wind signals
print('sw_r2p ', args.sw_r2p)
# if isinstance(args.sw_r2p,(float,int)):
# args.sw_r2p = [args.sw_r2p]
dp_thresh = parameter.Uniform(0,1)('k_threshold')
else:
dp_thresh = args.dp_thresh
pl = dropout.dropout_powerlaw(log10_A=log10_A, gamma=gamma,
k_drop=k_drop, k_threshold=dp_thresh)
rn_plaw = gp_signals.FourierBasisGP(pl, components=30,
Tspan=Tspan, name='red_noise')
else:
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=args.psd, prior='log-uniform',
Tspan=Tspan, gamma_val=args.gamma_gw, name='gw',
components=args.nfreqs,
delta_val=0.0)
base_model = tm
if args.bayes_ephem:
base_model += deterministic_signals.PhysicalEphemerisSignal(use_epoch_toas=True)
if args.rn_psrs[0]=='all':
rn_psrs='all'
else:
rn_psrs=args.rn_psrs
model1_psrs = []
model2a_psrs = []
if rn_psrs=='all':
model_1 = base_model + rn_plaw
### White Noise ###
wn = models.white_noise_block(vary=False, inc_ecorr=inc_ecorr)
### Red Noise ###
if args.gwb_ul:
prior = 'uniform'
else:
prior = 'log-uniform'
rn_plaw = models.red_noise_block(psd='powerlaw',
prior=prior,
Tspan=Tspan,
components=30,
gamma_val=None)
### GWB ###
gw = models.common_red_noise_block(psd='powerlaw',
prior=prior,
Tspan=Tspan,
gamma_val=None,
name='gw')
base_model = wn + gw
if args.bayes_ephem:
base_model += deterministic_signals.PhysicalEphemerisSignal(
use_epoch_toas=True)
model_plaw = base_model + rn_plaw
model_list = []
noise = {}
for psr in psrs:
if psr.name in args.free_spec_psrs:
model_list.append(model_fs(psr))
