def main():
args = parse_args()
process_args(args)
misc_utils.print_args(args)
if args.seed is not None:
np.random.seed(args.seed)
asimov_paramset, hypo_paramset = get_paramsets(args, define_nuisance())
prefix = ''
outfile = args.datadir + '/mc_unitary' + prefix + gen_identifier(args)
print('== {0:<25} = {1}'.format('outfile', outfile))
print('asimov_paramset', asimov_paramset)
print('hypo_paramset', hypo_paramset)
if args.run_mcmc:
ln_prob_eval = partial(
ln_prob,
hypo_paramset = hypo_paramset,
args = args,
)
if args.mcmc_seed_type == MCMCSeedType.UNIFORM:
p0 = mcmc_utils.flat_seed(
hypo_paramset, nwalkers=args.nwalkers
)
elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN:
p0 = mcmc_utils.gaussian_seed(
hypo_paramset, nwalkers=args.nwalkers
)
samples = mcmc_utils.mcmc(
p0 = p0,
ln_prob = ln_prob_eval,
ndim = len(hypo_paramset),
nwalkers = args.nwalkers,
burnin = args.burnin,
nsteps = args.nsteps,
threads = args.threads
)
mmxs = map(fr_utils.angles_to_u, samples)
frs = np.array(
[fr_utils.u_to_fr(args.source_ratio, x) for x in mmxs]
)
mcmc_utils.save_chains(frs, outfile)
print("DONE!")
def main():
args = parse_args()
process_args(args)
misc_utils.print_args(args)
if args.seed is not None:
np.random.seed(args.seed)
asimov_paramset, hypo_paramset = get_paramsets(args, define_nuisance())
prefix = ''
outfile = args.datadir + '/mc_x' + prefix
print('== {0:<25} = {1}'.format('outfile', outfile))
print('asimov_paramset', asimov_paramset)
print('hypo_paramset', hypo_paramset)
if args.run_mcmc:
ln_prob_eval = partial(
ln_prob,
hypo_paramset=hypo_paramset,
args=args,
)
if args.mcmc_seed_type == MCMCSeedType.UNIFORM:
p0 = mcmc_utils.flat_seed(hypo_paramset, nwalkers=args.nwalkers)
elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN:
p0 = mcmc_utils.gaussian_seed(hypo_paramset,
nwalkers=args.nwalkers)
samples = mcmc_utils.mcmc(p0=p0,
ln_prob=ln_prob_eval,
ndim=len(hypo_paramset),
nwalkers=args.nwalkers,
burnin=args.burnin,
nsteps=args.nsteps,
args=args,
threads=args.threads)
nsamples = len(samples)
srcs = [fr_utils.normalize_fr((x, 1 - x, 0)) for x in samples.T[-1]]
mmxs = map(fr_utils.angles_to_u, samples.T[:-1].T)
frs = np.array(
[fr_utils.u_to_fr(srcs[i], mmxs[i]) for i in range(nsamples)],
dtype=np.float64)
mcmc_utils.save_chains(frs, outfile)
print("DONE!")
def main():
args = parse_args()
process_args(args)
misc_utils.print_args(args)
if args.seed is not None:
np.random.seed(args.seed)
asimov_paramset, llh_paramset = get_paramsets(args, define_nuisance())
prefix = ''
outfile = args.datadir + '/mc_texture' + prefix + gen_identifier(args)
print('== {0:<25} = {1}'.format('outfile', outfile))
print('asimov_paramset', asimov_paramset)
print('llh_paramset', llh_paramset)
if args.run_mcmc:
ln_prob_eval = partial(
ln_prob,
llh_paramset=llh_paramset,
args=args,
)
if args.mcmc_seed_type == MCMCSeedType.UNIFORM:
p0 = mcmc_utils.flat_seed(llh_paramset, nwalkers=args.nwalkers)
elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN:
p0 = mcmc_utils.gaussian_seed(llh_paramset, nwalkers=args.nwalkers)
samples = mcmc_utils.mcmc(p0=p0,
ln_prob=ln_prob_eval,
ndim=len(llh_paramset),
nwalkers=args.nwalkers,
burnin=args.burnin,
nsteps=args.nsteps,
threads=args.threads)
frs = np.array(map(
lambda x: fr_utils.flux_averaged_BSMu(x, args, args.spectral_index,
llh_paramset), samples),
dtype=float)
frs_scale = np.vstack((frs.T, samples[:-1].T)).T
mcmc_utils.save_chains(frs_scale, outfile)
print("DONE!")
def main():
args = parse_args()
process_args(args)
misc_utils.print_args(args)
if args.seed is not None:
np.random.seed(args.seed)
asimov_paramset, llh_paramset = get_paramsets(args, define_nuisance())
outfile = args.datadir + '/{0}/{1}/chains_'.format(
*map(misc_utils.parse_enum,
[args.stat_method, args.data])) + misc_utils.gen_identifier(args)
print('== {0:<25} = {1}'.format('outfile', outfile))
if args.run_mcmc:
gf_utils.setup_fitter(args, asimov_paramset)
print('asimov_paramset', asimov_paramset)
print('llh_paramset', llh_paramset)
ln_prob = partial(llh_utils.ln_prob,
args=args,
asimov_paramset=asimov_paramset,
llh_paramset=llh_paramset)
if args.mcmc_seed_type == MCMCSeedType.UNIFORM:
p0 = mcmc_utils.flat_seed(llh_paramset, nwalkers=args.nwalkers)
elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN:
p0 = mcmc_utils.gaussian_seed(llh_paramset, nwalkers=args.nwalkers)
samples = mcmc_utils.mcmc(p0=p0,
ln_prob=ln_prob,
ndim=len(llh_paramset),
nwalkers=args.nwalkers,
burnin=args.burnin,
nsteps=args.nsteps,
threads=args.mcmc_threads)
mcmc_utils.save_chains(samples, outfile)
raw = np.load(outfile + '.npy')
raw[:, 4] *= 1E23
raw[:, 5] *= 1E21
ranges = list(llh_paramset.ranges)
ranges[4] = [x * 1E23 for x in ranges[4]]
ranges[5] = [x * 1E21 for x in ranges[5]]
labels = [
r'${\rm sin}^2\theta_{12}$',
r'${\rm cos}^4\theta_{13}$',
r'${\rm sin}^2\theta_{23}$',
r'$\delta$',
r'$\Delta m_{21}^2\left[10^{-5}\,{\rm eV}^2\right]$',
r'$\Delta m_{31}^2\left[10^{-3}\,{\rm eV}^2\right]$',
r'$N_{\rm conv}$',
r'$N_{\rm prompt}$',
r'$N_{\rm muon}$',
r'$N_{\rm astro}$',
r'$\gamma_{\rm astro}$',
r'${\rm log}_{10}\left[\Lambda^{-1}_{'+ \
r'{0}'.format(args.dimension)+r'}'+ \
misc_utils.get_units(args.dimension)+r'\right]$'
]
plot_utils.chainer_plot(infile=raw,
outfile=outfile[:5] +
outfile[5:].replace('data', 'plots'),
outformat=['pdf'],
args=args,
llh_paramset=llh_paramset,
labels=labels,
ranges=ranges)
print("DONE!")
def main():
args = parse_args()
process_args(args)
misc_utils.print_args(args)
if args.seed is not None:
np.random.seed(args.seed)
asimov_paramset, hypo_paramset = get_paramsets(args, define_nuisance())
hypo_paramset.extend(asimov_paramset.from_tag(ParamTag.BESTFIT))
prefix = ''
outfile = args.datadir + '/contour' + prefix + gen_identifier(args)
print('== {0:<25} = {1}'.format('outfile', outfile))
print('asimov_paramset', asimov_paramset)
print('hypo_paramset', hypo_paramset)
if args.run_mcmc:
gf_utils.setup_fitter(args, asimov_paramset)
ln_prob_eval = partial(
ln_prob,
hypo_paramset = hypo_paramset,
args = args,
)
if args.mcmc_seed_type == MCMCSeedType.UNIFORM:
p0 = mcmc_utils.flat_seed(
hypo_paramset, nwalkers=args.nwalkers
)
elif args.mcmc_seed_type == MCMCSeedType.GAUSSIAN:
p0 = mcmc_utils.gaussian_seed(
hypo_paramset, nwalkers=args.nwalkers
)
samples = mcmc_utils.mcmc(
p0 = p0,
ln_prob = ln_prob_eval,
ndim = len(hypo_paramset),
nwalkers = args.nwalkers,
burnin = args.burnin,
nsteps = args.nsteps,
threads = args.mcmc_threads
)
mcmc_utils.save_chains(samples, outfile)
labels = [
r'$N_{\rm conv}$',
r'$N_{\rm prompt}$',
r'$N_{\rm muon}$',
r'$N_{\rm astro}$',
r'$\gamma_{\rm astro}$',
r'$\text{sin}^4\phi_\oplus$',
r'$\text{cos}\left(2\psi_\oplus\right)$',
]
of = outfile[:5]+outfile[5:].replace('data', 'plots')+'_posterior'
plot_utils.chainer_plot(
infile = outfile+'.npy',
outfile = of,
outformat = ['pdf'],
args = args,
llh_paramset = hypo_paramset,
fig_text = gen_figtext(args),
labels = labels
)
print("DONE!")