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
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,
# components=5, gamma_val=4.33, name='gw', orf='hd')
crn_models = [s(psr) for psr in pkl_psrs]
# gw_models = [(m + gw)(psr) for psr,m in zip(final_psrs,psr_models)]
# 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, 0,
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')
rn_low = blocks.red_noise_block(psd='powerlaw', prior='log-uniform',
Tspan=Tspan_PTA, modes=modes, wgts=wgts,)
rn_std = blocks.red_noise_block(psd='powerlaw', prior='log-uniform',
Tspan=Tspan_PTA, components=30)
gamma_gw = parameter.Constant(4.3333)('gw_gamma')
log10_Agw = parameter.Uniform(-18, -14)('gw_log10_A')
plaw_low = gpp.powerlaw_genmodes(log10_A=log10_Agw,
gamma=gamma_gw,
wgts=wgts)
plaw_std = gpp.powerlaw(log10_A=log10_Agw, gamma=gamma_gw)
gw_std = gp_signals.FourierBasisCommonGP(plaw_std,
model_orfs.hd_orf(),
components=14,
Tspan=Tspan_PTA,
name='gw')
else:
sign_param = -1.0
wf = chrom_exp_decay(log10_Amp=log10_Amp_dmexp,
t0=t0_dmexp,
log10_tau=log10_tau_dmexp,
sign_param=sign_param,
idx=idx)
dmexp = deterministic_signals.Deterministic(wf, name=name)
return dmexp
Tspan = 407576851.48121357
rn = red_noise_block(psd='powerlaw',
prior='log-uniform',
Tspan=Tspan,
components=30,
gamma_val=None)
gw = common_red_noise_block(psd='powerlaw',
prior='log-uniform',
Tspan=Tspan,
components=5,
gamma_val=4.3333)
sig = rn + gw
index = parameter.Uniform(0, 2)
ppta_dip = dm_exponential_dip(57450,
57560,
idx=index,
Tspan_PTA = model_utils.get_tspan(pkl_psrs)
# common red noise block
cs = blocks.common_red_noise_block(psd='powerlaw',
prior='log-uniform',
Tspan=Tspan_PTA,
components=args.n_gwbfreqs,
gamma_val=args.gamma_gw,
name='gw')
# gw = blocks.common_red_noise_block(psd='powerlaw', prior='log-uniform', Tspan=Tspan_PTA,
# components=5, gamma_val=4.33, name='gw', orf='hd')
# timing model
s = gp_signals.MarginalizingTimingModel()
# intrinsic red noise
s += blocks.red_noise_block(prior='log-uniform',
Tspan=Tspan_PTA,
components=30)
# adding white-noise, separating out Adv Noise Psrs, and acting on psr objects
final_psrs = []
psr_models = []
### Add a stand alone SW deter model
bins = np.linspace(53215, 57934, 26)
bins *= 24 * 3600 #Convert to secs
n_earth = chrom.solar_wind.ACE_SWEPAM_Parameter(size=bins.size -
1)('n_earth')
deter_sw = chrom.solar_wind.solar_wind(n_earth=n_earth, n_earth_bins=bins)
mean_sw = deterministic_signals.Deterministic(deter_sw, name='sw_r2')
np_earth = parameter.Uniform(-4, -2)('np_4p39')
sw_power = parameter.Constant(4.39)('sw_power_4p39')
deter_sw_p = chrom.solar_wind.solar_wind_r_to_p(n_earth=np_earth,
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',
prior='log-uniform',
Tspan=Tspan,
components=args.n_gwbfreqs,
gamma_val=args.gamma_gw,
