def sample_one_sigma_gaussian(result):
par_ranges = result.intervals
new_pars = [
prior.updated(result.model.parameters[p.name], p) for p in par_ranges
]
return np.vstack([[p.sample(size=result.strategy.nwalkers)]
for p in new_pars]).T
def test_updated():
p = prior.BoundedGaussian(1, 2, -1, 2)
d = UncertainValue(1, 0.5, 1)
u = prior.updated(p, d)
assert_equal(u.guess, 1)
assert_obj_close(u.lnprob(0), gold_sigma)
def test_updated():
p = BoundedGaussian(1, 2, -1, 2)
d = UncertainValue(1, 0.5, 1)
u = updated(p, d)
assert_equal(u.guess, 1)
assert_allclose(u.lnprob(0), GOLD_SIGMA)
def test_updated():
p=prior.BoundedGaussian(1,2,-1,2)
d=mcmc.UncertainValue(1,0.5,1)
u=prior.updated(p,d)
assert_equal(u.guess,1)
assert_obj_close(u.lnprob(0),gold_sigma)