def main(args=None):
path = os.path.abspath(__file__).rsplit('/', 1)[0]
defaults = parse_config(path + '/default.cfg')
opt = parse_config('model.cfg', defaults)
print '### Read parameters from model.cfg ###'
print 'model parameters', P['names']
print 'minimum allowed values', P['min']
print 'maximum allowed values', P['max']
Npar = len(P['names'])
print opt.Nthreads, 'threads'
print opt.Nwalkers, 'walkers'
sampler = emcee.EnsembleSampler(opt.Nwalkers,
Npar,
ln_likelihood,
threads=opt.Nthreads)
if opt.Nburn > 0:
t1 = time.time()
p0 = get_initial_positions(opt.Nwalkers)
run_burn_in(sampler, opt, p0)
print '%.2g min elapsed' % ((time.time() - t1) / 60.)
if opt.Nmcmc > 0:
t1 = time.time()
run_mcmc(sampler, opt)
print '%.2g min elapsed' % ((time.time() - t1) / 60.)
return sampler
def main(args=None):
path = os.path.abspath(__file__).rsplit('/', 1)[0]
defaults = parse_config(path + '/default.cfg')
opt = parse_config('model.cfg', defaults)
print '### Read parameters from model.cfg ###'
print 'model parameters', P['names']
print 'minimum allowed values', P['min']
print 'maximum allowed values', P['max']
Npar = len(P['names'])
print opt.Nthreads, 'threads'
print opt.Nwalkers, 'walkers'
sampler = emcee.EnsembleSampler(
opt.Nwalkers, Npar, ln_likelihood, threads=opt.Nthreads)
if opt.Nburn > 0:
t1 = time.time()
p0 = get_initial_positions(opt.Nwalkers)
run_burn_in(sampler, opt, p0)
print '%.2g min elapsed' % ((time.time() - t1)/60.)
if opt.Nmcmc > 0:
t1 = time.time()
run_mcmc(sampler, opt)
print '%.2g min elapsed' % ((time.time() - t1)/60.)
return sampler
# if you get an error when running emcee in parallel, you often get an
# unhelpful error messgae, making debugging very difficult. To check
# everything is working before you start running emcee, use thsi test
# file.
from model import \
ln_likelihood, P, x, ydata, ysigma, get_initial_positions, plot_model
p0 = get_initial_positions(1)
plot_model(p0)
