# get evidence
hdf = h5py.File(outpost, "r")
a = hdf["lalinference"]["lalinference_nest"]
evsig = a.attrs["log_evidence"]
evnoise = a.attrs["log_noise_evidence"]
hdf.close()
# run bilby via the pe interface
runner = pe(
data_file=hetfile,
par_file=parfile,
prior=priors,
detector=detector,
sampler="dynesty",
sampler_kwargs={
"Nlive": Nlive,
"walks": 40
},
outdir=outdir,
label=label,
)
result = runner.result
# evaluate the likelihood on a grid
gridpoints = 35
grid_size = dict()
for p in priors.keys():
grid_size[p] = np.linspace(np.min(result.posterior[p]),
np.max(result.posterior[p]), gridpoints)
postsamples[:, i] = post[p.upper()]
# get evidence
hdf = h5py.File(outpost, "r")
a = hdf["lalinference"]["lalinference_nest"]
evsig = a.attrs["log_evidence"]
evnoise = a.attrs["log_noise_evidence"]
hdf.close()
# run bilby via the pe interface
runner = pe(
data_file_1f=hetfiles[0],
data_file_2f=hetfiles[1],
par_file=parfile,
prior=priors,
detector=detector,
sampler="dynesty",
sampler_kwargs={"Nlive": Nlive, "walks": 60},
outdir=outdir,
label=label,
)
result = runner.result
# output comparisons
comparisons_two_harmonics(label, outdir, priors, cred=0.9)
# plot results
fig = result.plot_corner(save=False, parameters=list(priors.keys()), color="b")
fig = corner.corner(
postsamples,
postsamples = np.zeros((lp, len(priors)))
for i, p in enumerate(priors.keys()):
postsamples[:, i] = post[p.upper()]
# get evidence
hdf = h5py.File(outpost, "r")
a = hdf["lalinference"]["lalinference_nest"]
evsig = a.attrs["log_evidence"]
evnoise = a.attrs["log_noise_evidence"]
hdf.close()
# run bilby via the pe interface
runner = pe(
data_file=hetfiles,
par_file=parfile,
prior=priors,
detector=detectors,
outdir=outdir,
label=label,
)
result = runner.result
# evaluate the likelihood on a grid
gridpoints = 35
grid_size = dict()
for p in priors.keys():
grid_size[p] = np.linspace(np.min(result.posterior[p]),
np.max(result.posterior[p]), gridpoints)
grunner = pe(
data_file=hetfiles,
with open(parfile, "w") as fp:
fp.write(parcontent)
# use pe to create the data and sample on a grid
detector = "H1" # the detector to use
times = np.linspace(1000000000.0, 1000086340.0, 1440) # times
asd = 1e-24
# set prior on h0
priors = dict()
priors["h0"] = Uniform(0.0, 1e-24, "h0")
h0s = np.linspace(0.0, 1e-24, 500) # h0 values to evaluate at
run = pe(
detector=detector,
fake_times=times,
par_file=parfile,
inj_par=parfile,
fake_asd=asd,
grid=True,
grid_kwargs={"grid_size": {
"h0": h0s
}},
prior=priors,
)
pl.plot(h0s, np.exp(run.grid.ln_posterior - np.max(run.grid.ln_posterior)),
"b")
pl.axvline(run.hetdata["H1"][0].par["H0"])
pl.show()
# get evidence
hdf = h5py.File(outpost, "r")
a = hdf["lalinference"]["lalinference_nest"]
evsig = a.attrs["log_evidence"]
evnoise = a.attrs["log_noise_evidence"]
hdf.close()
# run bilby via the pe interface
runner = pe(
data_file=hetfiles,
par_file=parfile,
prior=priors,
detector=detectors,
sampler="dynesty",
sampler_kwargs={
"Nlive": Nlive,
"walks": 40,
"use_ratio": True
},
outdir=outdir,
label=label,
numba=True,
)
result = runner.result
# evaluate the likelihood on a grid
gridpoints = 35
grid_size = dict()
for p in priors.keys():
grid_size[p] = np.linspace(np.min(result.posterior[p]),
