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semi_coherent_search_using_MCMC.py
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Gregory Ashton / PyFstat
442 commits behind the upstream repository.
Gregory Ashton authored
- Also changes the parameter vector in the normal fully coherent search to remove the tstart and tend. This is done by wrappin the run_computefstatistic_single_point in a compute_fullycoherent_det_stat function - Removes theta0 from the saved data dict for the MCMCSearch - Adds example usage of the semi-coherent search
semi_coherent_search_using_MCMC.py 891 B
import pyfstat
F0 = 30.0
F1 = -1e-10
F2 = 0
Alpha = 5e-3
Delta = 6e-2
tref = 362750407.0
tstart = 1000000000
duration = 100*86400
tend = tstart + duration
theta_prior = {'F0': {'type': 'unif', 'lower': F0*(1-1e-6), 'upper': F0*(1+1e-5)},
'F1': {'type': 'unif', 'lower': F1*(1+1e-2), 'upper': F1*(1-1e-2)},
'F2': F2,
'Alpha': Alpha,
'Delta': Delta
}
ntemps = 1
log10temperature_min = -1
nwalkers = 100
nsteps = [500, 500, 500]
mcmc = pyfstat.MCMCSemiCoherentSearch(
label='semi_coherent_search_using_MCMC', outdir='data', nsegs=20,
sftfilepath='data/*basic*sft', theta_prior=theta_prior, tref=tref,
minStartTime=tstart, maxStartTime=tend, nsteps=nsteps, nwalkers=nwalkers,
ntemps=ntemps, log10temperature_min=log10temperature_min)
mcmc.run()
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()