def test_model3a_broken_plaw(dmx_psrs, caplog):
caplog.set_level(logging.CRITICAL)
m3a = models.model_3a(dmx_psrs,
psd='broken_powerlaw',
delta_common=0,
noisedict=noise_dict)
assert hasattr(m3a, 'get_lnlikelihood')
def test_model3a_pshift(dmx_psrs, caplog):
caplog.set_level(logging.CRITICAL)
m3a = models.model_3a(dmx_psrs,
noisedict=noise_dict,
pshift=True,
pseed=42)
assert hasattr(m3a, 'get_lnlikelihood')
def test_hypermodel(dmx_psrs, caplog):
m2a = models.model_2a(dmx_psrs, noisedict=noise_dict)
m3a = models.model_3a(dmx_psrs, noisedict=noise_dict)
ptas = {0: m2a, 1: m3a}
hm = hypermodel.HyperModel(ptas)
assert hasattr(hm, 'get_lnlikelihood')
assert 'gw_log10_A' in hm.param_names
assert 'nmodel' in hm.param_names
def test_hyper_sampler(dmx_psrs, caplog):
m2a = models.model_2a(dmx_psrs, noisedict=noise_dict)
m3a = models.model_3a(dmx_psrs, noisedict=noise_dict)
ptas = {0: m2a, 1: m3a}
hm = hypermodel.HyperModel(ptas)
samp = hm.setup_sampler(outdir=outdir, human='tester')
assert hasattr(samp, "sample")
paramfile = os.path.join(outdir, "pars.txt")
assert os.path.isfile(paramfile)
with open(paramfile, "r") as f:
params = [line.rstrip('\n') for line in f]
for ptapar, filepar in zip(hm.param_names, params):
assert ptapar == filepar
x0 = hm.initial_sample()
assert len(x0) == len(hm.param_names)
def test_model3a_5rnfreqs(dmx_psrs, caplog):
caplog.set_level(logging.CRITICAL)
m3a = models.model_3a(dmx_psrs, n_rnfreqs=5, noisedict=noise_dict)
assert hasattr(m3a, 'get_lnlikelihood')
scr_npz = np.load(args.sky_scramble)
thetas = scr_npz['thetas'][args.process, :]
phis = scr_npz['phis'][args.process, :]
for p, theta, phi in zip(psrs, thetas, phis):
print(p.name, ': ', p.theta, p.phi, p._raj, p._decj, p._pos)
p._raj = phi
p._decj = np.pi / 2 - theta
p._pos = p._get_pos()
print(p.name, ': ', p.theta, p.phi, p._raj, p._decj, p._pos)
pta_gw = models.model_3a(psrs,
psd=args.psd,
noisedict=noise,
n_gwbfreqs=args.nfreqs,
gamma_common=args.gamma_gw,
delta_common=0.,
upper_limit=False,
bayesephem=args.bayes_ephem,
be_type='setIII',
wideband=False,
rn_psrs=rn_psrs,
pshift=args.pshift)
ptas = {0: pta_crn, 1: pta_gw}
if args.model_wts is None:
model_wts = None
else:
model_wts = dict(enumerate(args.model_wts))
hm = hypermodel.HyperModel(models=ptas, log_weights=model_wts)
c0 = co.HyperModelCore(args.outdir, chaindir=args.outdir)
with open(args.noisepath, 'r') as fin:
noise = json.load(fin)
if args.rn_psrs[0] == 'all':
rn_psrs = 'all'
else:
rn_psrs = args.rn_psrs
pta_gw = models.model_3a(psrs,
psd='powerlaw',
noisedict=noise,
n_gwbfreqs=args.nfreqs,
gamma_common=13 / 3.,
upper_limit=False,
bayesephem=args.bayes_ephem,
be_type='setIII',
wideband=False,
rn_psrs=rn_psrs,
pshift=args.pshift,
pseed=args.process,
psr_models=False)
pta_crn = models.model_2a(psrs,
psd='powerlaw',
noisedict=noise,
n_gwbfreqs=args.nfreqs,
gamma_common=13 / 3.,
upper_limit=False,
bayesephem=args.bayes_ephem,
be_type='setIII',
pta = models.model_2a(psrs,
psd=args.psd,
noisedict=noise_dict,
n_gwbfreqs=args.nfreqs,
gamma_common=gamma_gw,
upper_limit=args.gwb_ul,
bayesephem=args.bayes_ephem,
wideband=args.wideband,
pshift=args.pshift,
select='backend')
if args.model == 'model_3a':
pta = models.model_3a(psrs,
psd=args.psd,
noisedict=noise_dict,
n_gwbfreqs=args.nfreqs,
gamma_common=gamma_gw,
upper_limit=args.gwb_ul,
bayesephem=args.bayes_ephem,
wideband=args.wideband,
pshift=args.pshift,
select='backend')
elif args.model == 'model_general':
pta = models.model_general(
psrs,
common_psd='powerlaw',
red_psd=args.psd,
orf=None,
common_components=args.nfreqs,
red_components=30,
dm_components=30,
modes=None,
wgts=None,
