def convergence_plots(sampler):
plt.figure()
plt.plot(sampler.lnprobability.T)
plt.figure()
pu.plot_emcee_chains(sampler.chain)
triangle.corner(sampler.flatchain)
print 'Autocorrelation lengths: ', ac.emcee_chain_autocorrelation_lengths(sampler.chain)
print 'Gelman-Rubin R: ', ac.emcee_gelman_rubin_r(sampler.chain)
def convergence_plots(sampler):
plt.figure()
plt.plot(sampler.lnprobability.T)
plt.figure()
pu.plot_emcee_chains(sampler.chain)
triangle.corner(sampler.flatchain)
print 'Autocorrelation lengths: ', ac.emcee_chain_autocorrelation_lengths(
sampler.chain)
print 'Gelman-Rubin R: ', ac.emcee_gelman_rubin_r(sampler.chain)
sampler = emcee.EnsembleSampler(args.walkers, logpost.nparams,
logpost, threads=args.threads)
with bz2.BZ2File(op.join(args.outdir, 'chain.npy.bz2'), 'r') as inp:
chain = np.load(inp)
with bz2.BZ2File(op.join(args.outdir, 'lnprob.npy.bz2'), 'r') as inp:
lnprob = np.load(inp)
sampler._chain = chain
sampler._lnprob = lnprob
while True:
sampler.run_mcmc(sampler.chain[:,-1,:], args.nstep, thin=args.nthin)
print 'Saving state after ', sampler.chain.shape[1], ' samples....'
print 'Autocorrelations are ', ac.emcee_chain_autocorrelation_lengths(sampler.chain, fburnin=args.fburnin)
chain_name = uuid.uuid4().hex
lnprob_name = uuid.uuid4().hex
chain_name = chain_name + '.npy.bz2'
lnprob_name = lnprob_name + '.npy.bz2'
chain_file = op.join(args.outdir, chain_name)
lnprob_file = op.join(args.outdir, lnprob_name)
with bz2.BZ2File(chain_file, 'w') as out:
np.save(out, sampler.chain)
with bz2.BZ2File(lnprob_file, 'w') as out:
np.save(out, sampler.lnprobability)
def acls(self):
"""Return the estimate of the current chain's autocorrelation lengths,
using :func:`plotutils.autocorr.emcee_chain_autocorrelation_lengths`.
"""
return ac.emcee_chain_autocorrelation_lengths(self.chain)
