def xy_completed_bayesian_analysis(xy_fitted_joint_likelihood):
jl, _, _ = xy_fitted_joint_likelihood
jl.restore_best_fit()
model = jl.likelihood_model
data = jl.data_list
model.fake.spectrum.main.composite.a_1.set_uninformative_prior(
Uniform_prior)
model.fake.spectrum.main.composite.b_1.set_uninformative_prior(
Uniform_prior)
model.fake.spectrum.main.composite.F_2.set_uninformative_prior(
Log_uniform_prior)
model.fake.spectrum.main.composite.mu_2.set_uninformative_prior(
Uniform_prior)
model.fake.spectrum.main.composite.sigma_2.set_uninformative_prior(
Log_uniform_prior)
bs = BayesianAnalysis(model, data)
bs.set_sampler("emcee")
bs.sampler.setup(n_burn_in=100, n_iterations=100, n_walkers=20)
samples = bs.sample()
return bs, samples
def completed_bn090217206_bayesian_analysis_multicomp(
fitted_joint_likelihood_bn090217206_nai_multicomp, ):
jl, _, _ = fitted_joint_likelihood_bn090217206_nai_multicomp
# This is necessary because other tests/functions might have messed up with the
# model stored within
jl.restore_best_fit()
model = jl.likelihood_model
data_list = jl.data_list
spectrum = jl.likelihood_model.bn090217206.spectrum.main.shape
spectrum.index_1.prior = Uniform_prior(lower_bound=-5.0, upper_bound=5.0)
spectrum.K_1.prior = Log_uniform_prior(lower_bound=1.0, upper_bound=10)
spectrum.K_2.prior = Log_uniform_prior(lower_bound=1e-20, upper_bound=10)
spectrum.kT_2.prior = Log_uniform_prior(lower_bound=1e0, upper_bound=1e3)
bayes = BayesianAnalysis(model, data_list)
bayes.set_sampler("emcee")
bayes.sampler.setup(n_walkers=50,
n_burn_in=50,
n_iterations=100,
seed=1234)
samples = bayes.sample()
return bayes, bayes.samples
def completed_bn090217206_bayesian_analysis(
fitted_joint_likelihood_bn090217206_nai):
jl, _, _ = fitted_joint_likelihood_bn090217206_nai
jl.restore_best_fit()
model = jl.likelihood_model
data_list = jl.data_list
powerlaw = jl.likelihood_model.bn090217206.spectrum.main.Powerlaw
powerlaw.index.prior = Uniform_prior(lower_bound=-5.0, upper_bound=5.0)
powerlaw.K.prior = Log_uniform_prior(lower_bound=1.0, upper_bound=10)
bayes = BayesianAnalysis(model, data_list)
bayes.set_sampler("emcee")
bayes.sampler.setup(n_walkers=50,
n_burn_in=50,
n_iterations=100,
seed=1234)
samples = bayes.sample()
return bayes, samples
def test_ubinned_poisson_full(event_observation_contiguous, event_observation_split):
s = Line()
ps = PointSource("s", 0, 0, spectral_shape=s)
s.a.bounds = (0, None)
s.a.value = .1
s.b.value = .1
s.a.prior = Log_normal(mu=np.log(10), sigma=1)
s.b.prior = Gaussian(mu=0, sigma=1)
m = Model(ps)
######
######
######
ub1 = UnbinnedPoissonLike("test", observation=event_observation_contiguous)
jl = JointLikelihood(m, DataList(ub1))
jl.fit(quiet=True)
np.testing.assert_allclose([s.a.value, s.b.value], [6.11, 1.45], rtol=.5)
ba = BayesianAnalysis(m, DataList(ub1))
ba.set_sampler("emcee")
ba.sampler.setup(n_burn_in=100, n_walkers=20, n_iterations=500)
ba.sample(quiet=True)
ba.restore_median_fit()
np.testing.assert_allclose([s.a.value, s.b.value], [6.11, 1.45], rtol=.5)
######
######
######
ub2 = UnbinnedPoissonLike("test", observation=event_observation_split)
jl = JointLikelihood(m, DataList(ub2))
jl.fit(quiet=True)
np.testing.assert_allclose([s.a.value, s.b.value], [2., .2], rtol=.5)
ba = BayesianAnalysis(m, DataList(ub2))
ba.set_sampler("emcee")
ba.sampler.setup(n_burn_in=100, n_walkers=20, n_iterations=500)
ba.sample(quiet=True)
ba.restore_median_fit()
np.testing.assert_allclose([s.a.value, s.b.value], [2., .2], rtol=.5)