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))