def __init__(self, psrs, params=None,
psrTerm=True, bayesephem=True, pta=None):
if pta is None:
# initialize standard model with fixed white noise
# and powerlaw red noise
# uses the implementation of ECORR in gp_signals
print('Initializing the model...')
tmin = np.min([p.toas.min() for p in psrs])
tmax = np.max([p.toas.max() for p in psrs])
Tspan = tmax - tmin
s = deterministic.cw_block_circ(amp_prior='log-uniform',
psrTerm=psrTerm, tref=tmin, name='cw')
s += gp_signals.TimingModel()
s += blocks.red_noise_block(prior='log-uniform', psd='powerlaw',
Tspan=Tspan, components=30)
if bayesephem:
s += deterministic_signals.PhysicalEphemerisSignal(use_epoch_toas=True)
# adding white-noise, and acting on psr objects
models = []
for p in psrs:
if 'NANOGrav' in p.flags['pta']:
s2 = s + blocks.white_noise_block(vary=False, inc_ecorr=True,
gp_ecorr=True)
models.append(s2(p))
else:
s3 = s + blocks.white_noise_block(vary=False, inc_ecorr=False)
models.append(s3(p))
pta = signal_base.PTA(models)
# set white noise parameters
if params is None:
print('No noise dictionary provided!')
else:
pta.set_default_params(params)
self.pta = pta
else:
# user can specify their own pta object
# if ECORR is included, use the implementation in gp_signals
self.pta = pta
self.psrs = psrs
self.params = params
self.Nmats = self.get_Nmats()
def pta_pshift(dmx_psrs, caplog):
Tspan = model_utils.get_tspan(dmx_psrs)
tm = gp_signals.TimingModel()
wn = blocks.white_noise_block(inc_ecorr=True, tnequad=True)
rn = blocks.red_noise_block(Tspan=Tspan)
pseed = parameter.Uniform(0, 10000)('gw_pseed')
gw_log10_A = parameter.Uniform(-18, -14)('gw_log10_A')
gw_gamma = parameter.Constant(13. / 3)('gw_gamma')
gw_pl = utils.powerlaw(log10_A=gw_log10_A, gamma=gw_gamma)
gw_pshift = gp_signals.FourierBasisGP(spectrum=gw_pl,
components=5,
Tspan=Tspan,
name='gw',
pshift=True,
pseed=pseed)
model = tm + wn + rn + gw_pshift
pta_pshift = signal_base.PTA([model(p) for p in dmx_psrs])
pta_pshift.set_default_params(noise_dict)
return pta_pshift
with open(args.noisepath, 'r') as fin:
noise = json.load(fin)
# Set Tspan for RN
Tspan_PTA = model_utils.get_tspan(pkl_psrs)
# common red noise block
fmin = 10.0
modes, wgts = model_utils.linBinning(Tspan_PTA, 1, 1.0 / fmin / Tspan_PTA,
14, 5)
# wgts = wgts**2.0
# timing model
s = gp_signals.MarginalizingTimingModel()
s += blocks.white_noise_block(vary=False, inc_ecorr=True, select='backend')
s += blocks.red_noise_block(
psd='powerlaw',
prior='log-uniform',
Tspan=Tspan_PTA,
modes=modes,
wgts=wgts,
)
gamma_gw = parameter.Uniform(0, 7)('gw_gamma')
log10_Agw = parameter.Uniform(-18, -11)('gw_log10_A')
cpl = gpp.powerlaw_genmodes(log10_A=log10_Agw, gamma=gamma_gw, wgts=wgts)
s += gp_signals.FourierBasisGP(cpl, modes=modes, name='gw_crn')
# gw = blocks.common_red_noise_block(psd='powerlaw', prior='log-uniform', Tspan=Tspan_PTA,
noise_fs.update(json.load(fin))
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
### Timing Model ###
tm = gp_signals.TimingModel()
### White Noise ###
wn = blocks.white_noise_block(vary=False, inc_ecorr=inc_ecorr)
### Red Noise ###
rn_plaw = blocks.red_noise_block(psd='powerlaw',
prior='log-uniform',
Tspan=Tspan,
components=30,
gamma_val=None)
rn_fs = blocks.red_noise_block(psd='spectrum',
prior='log-uniform',
Tspan=Tspan,
components=30,
gamma_val=None)
### GWB ###
gw = blocks.common_red_noise_block(psd='powerlaw',
psrs = pickle.load(fin)
psr = psrs[args.process]
print(f'Starting {psr.name}.')
with open(args.noisepath, 'r') as fin:
noise = json.load(fin)
if args.tspan is None:
Tspan = model_utils.get_tspan([psr])
else:
Tspan = args.tspan
tm = gp_signals.TimingModel()
log10_rho = parameter.Uniform(-10, -4, size=30)
fs = gp_priors.free_spectrum(log10_rho=log10_rho)
wn = blocks.white_noise_block(inc_ecorr=True)
log10_A = parameter.Constant()
gamma = parameter.Constant()
plaw_pr = gp_priors.powerlaw(log10_A=log10_A, gamma=gamma)
plaw = gp_signals.FourierBasisGP(plaw_pr, components=30, Tspan=Tspan)
rn = gp_signals.FourierBasisGP(fs,
components=30,
Tspan=Tspan,
name='excess_noise')
m = tm + wn + plaw + rn
if args.gwb_on:
gw_log10_A = parameter.Constant('gw_log10_A')
gw_gamma = parameter.Constant(4.3333)('gw_gamma')
gw_pr = gp_priors.powerlaw(log10_A=gw_log10_A, gamma=gw_gamma)
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
model_2a = model_1 + gw
model_1_ec = model_1 + blocks.white_noise_block(vary=False, inc_ecorr=True)
model_1 += blocks.white_noise_block(vary=False, inc_ecorr=False)
model_2a_ec = model_2a + blocks.white_noise_block(vary=False, inc_ecorr=True)
model_2a += blocks.white_noise_block(vary=False, inc_ecorr=False)
for p in psrs:
if 'NANOGrav' in p.flags['pta'] and not args.wideband:
model1_psrs.append(model_1_ec(p))
model2a_psrs.append(model_2a_ec(p))
else:
model1_psrs.append(model_1(p))
model2a_psrs.append(model_2a(p))
elif isinstance(rn_psrs,list):
model1_psrs = []
model2a_psrs = []