def test_mh(setup):
setup.fit.method = NelderMead()
setup.fit.stat = Cash()
setup.fit.fit()
results = setup.fit.est_errors()
cov = results.extra_output
mcmc = sim.MCMC()
for par in setup.fit.model.pars:
mcmc.set_prior(par, sim.flat)
prior = mcmc.get_prior(par)
assert prior.__name__ == 'flat'
mcmc.set_sampler('MH')
opt = mcmc.get_sampler_opt('defaultprior')
mcmc.set_sampler_opt('defaultprior', opt)
# mcmc.set_sampler_opt('verbose', True)
log = logging.getLogger("sherpa")
level = log.level
log.setLevel(logging.ERROR)
try:
stats, accept, params = mcmc.get_draws(setup.fit, cov, niter=1e2)
finally:
log.setLevel(level)
def test_list_samplers():
"""Ensure list_samplers returns a list."""
mcmc = sim.MCMC()
samplers = mcmc.list_samplers()
assert isinstance(samplers, list)
assert len(samplers) > 0
def test_list_samplers_contents():
"""Are the expected values included"""
# Test that the expected values exist in this list,
# but do not enforce these are the only values.
#
samplers = sim.MCMC().list_samplers()
for expected in ['mh', 'metropolismh']:
assert expected in samplers
def test_metropolisMH(self):
self.fit.method = NelderMead()
self.fit.stat = CStat()
results = self.fit.fit()
results = self.fit.est_errors()
cov = results.extra_output
mcmc = sim.MCMC()
mcmc.set_sampler('MetropolisMH')
# mcmc.set_sampler_opt('verbose', True)
log = logging.getLogger("sherpa")
level = log.level
log.setLevel(logging.ERROR)
stats, accept, params = mcmc.get_draws(self.fit, cov, niter=1e2)
log.setLevel(level)