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Gregory Ashton authoredThis removes the convergence testing ideas previously implemented (currently juts commented, but later to be fully removed). These are clearly not useful without further study, which in itself would be a better time to develop an implementation.
Gregory Ashton authoredThis removes the convergence testing ideas previously implemented (currently juts commented, but later to be fully removed). These are clearly not useful without further study, which in itself would be a better time to develop an implementation.
fully_coherent_search.py 787 B
from pyfstat import MCMCSearch
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': 'norm', 'loc': F0, 'scale': abs(1e-6*F0)},
'F1': {'type': 'norm', 'loc': F1, 'scale': abs(1e-6*F1)},
'F2': F2,
'Alpha': Alpha,
'Delta': Delta
}
ntemps = 1
nwalkers = 100
nsteps = [100, 500, 1000]
mcmc = MCMCSearch('fully_coherent', 'data', sftfilepath='data/*basic*sft',
theta_prior=theta_prior, tref=tref, tstart=tstart, tend=tend,
nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
scatter_val=1e-10)
mcmc.run()
mcmc.plot_corner()
mcmc.print_summary()