# 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]),