def lnprob(theta, x, y, yerr, bm, bp):
lp = lnprior(theta, bm, bp)
if not np.isfinite(lp):
return -np.inf
try:
return lp + binary_lnlike(theta, x, y, yerr)
except:
print theta
raise
# A_1, l2_1, l1_1, s, P_1, A_2, l2_2, l1_2, P_2
# theta = [-2., -2., -1.2, 1., np.log(.66), -2., -2., -1.2, -1.2, np.log(.97)]
# better initialisation
theta = [-7., -3., -7., 1., np.log(.66), -3.5, -2., -2., -1.6, np.log(.97)]
# plot data and prediction
pl.clf()
pl.errorbar(x_sub, y_sub, yerr=yerr_sub, fmt='k.', capsize=0)
xs = np.linspace(min(x_sub), max(x_sub), 100)
pl.plot(xs, binary_predict(xs, x_sub, y_sub, yerr_sub, theta)[0], color='.7')
pl.savefig('binary_data')
# Compute initial likelihood
print 'initial lnlike = ', binary_lnlike(theta, x, y, yerr)
# bm, bp = minimum and maximum periods
bm, bp = np.log(.2), np.log(2)
# Sample the posterior probability for m.
nwalkers, ndim = 64, len(theta)
p0 = [theta+1e-4*np.random.rand(ndim) for i in range(nwalkers)]
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args = (x, y, yerr, bm, bp))
# print("Burn-in")
# p0, lp, state = sampler.run_mcmc(p0, 200)
# sampler.reset()
nstep = 2000
nruns = 100.
