def per_iteration(lmparams, i, resids, mod_pars, photo_data, rv_data, *args, **kws):
if i%10 == 0.0:
ncores, fname = args
params = utilfuncs.get_lmfit_parameters(mod_pars, lmparams)
redchisqr = utilfuncs.reduced_chisqr(mod_pars, params, photo_data, rv_data)
utilfuncs.iterprint(mod_pars, params, 0.0, redchisqr, 0.0, 0.0)
utilfuncs.report_as_input(mod_pars, params, fname)
def per_iteration(mod_pars, theta, lnl, model):
global max_lnlike
if lnl > max_lnlike:
max_lnlike = lnl
params = utilfuncs.split_parameters(theta, mod_pars[0])
redchisqr = np.sum(((photo_data[1] - model) / photo_data[2]) ** 2) / \
(photo_data[1].size - 1 - (mod_pars[0] * 5 + (mod_pars[0] - 1) * 6))
utilfuncs.iterprint(mod_pars, params, max_lnlike, redchisqr, 0.0, 0.0)
utilfuncs.report_as_input(mod_pars, params, fname)
def generate(mod_pars, body_pars, photo_data, rv_data, nwalkers, ncores, fname, niterations=500):
# Flatten body parameters
theta = np.array(list(itertools.chain.from_iterable(body_pars)))
# Set up the sampler.
ndim = len(theta)
theta[theta == 0.0] = 1.0e-10
pos0 = [theta + theta * 1.0e-3 * np.random.randn(ndim) for i in range(nwalkers)]
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, args=(mod_pars, photo_data, rv_data), threads=ncores)
# Clear and run the production chain.
print("Running MCMC...")
# Make sure paths exist
if not os.path.exists("./output/{0}/reports".format(fname)):
os.makedirs(os.path.join("./", "{0}".format(fname), "output", "reports"))
# Setup some values for tracking time and completion
citer, tlast, tsum = 0.0, time.time(), []
for pos, lnp, state in sampler.sample(pos0, iterations=niterations, storechain=True):
# Save out the chain for later analysis
with open("./output/{0}/reports/mcmc_chain.dat".format(fname), "a+") as f:
for k in range(pos.shape[0]):
f.write("{0:4d} {1:s}\n".format(k, " ".join(map(str, pos[k]))))
citer += 1.0
tsum.append(time.time() - tlast)
tleft = np.median(tsum) * (niterations - citer)
tlast = time.time()
maxlnprob = np.argmax(lnp)
bestpos = pos[maxlnprob, :]
params = utilfuncs.split_parameters(bestpos, mod_pars[0])
redchisqr = utilfuncs.reduced_chisqr(mod_pars, params, photo_data, rv_data)
utilfuncs.iterprint(mod_pars, params, lnp[maxlnprob], redchisqr, citer / niterations, tleft)
utilfuncs.report_as_input(mod_pars, params, fname)
# Remove 'burn in' region
print('Burning in; creating sampler chain...')
burnin = int(0.5 * niterations)
samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))
# Compute the quantiles.
print('Computing quantiles; mapping results...')
results = map(
lambda v: (v[1], v[2] - v[1], v[1] - v[0]),
zip(*np.percentile(samples, [16, 50, 84],
axis=0))
)
# Produce final model and save the values
print('Saving final results...')
utilfuncs.mcmc_report_out(mod_pars, results, fname)
utilfuncs.plot_out(params, fname, sampler, samples, ndim)