def test_run_pyBAMBI_polychord():
loglikelihood.called = False
prior.called = False
pybambi.run_pyBAMBI(loglikelihood,
prior,
nDims,
nested_sampler='polychord',
root='chains/polychord',
nlive=50)
assert (loglikelihood.called is True)
assert (prior.called is True)
def test_run_pyBAMBI_polychord():
loglikelihood.called = False
prior.called = False
try:
os.makedirs('./chains/clusters/')
except:
pass
pybambi.run_pyBAMBI(loglikelihood,
prior,
nDims,
nested_sampler='polychord',
root='.chains/polychord',
nlive=50)
assert (loglikelihood.called == True)
assert (prior.called == True)
try:
shutil.rmtree('.chains')
except:
pass
def test_run_pyBAMBI_inputs():
with pytest.raises(NotImplementedError):
pybambi.run_pyBAMBI(0, 0, 0, nested_sampler='NAN')
with pytest.raises(TypeError):
pybambi.run_pyBAMBI(0, 0, 0, wrong_kwarg='foo')
from pybambi import run_pyBAMBI
from numpy import pi, log, sqrt
from mpi4py import MPI
nDims = 3
def loglikelihood(theta):
""" Spherical Gaussian Likelihood """
sigma = 0.1
nDims = len(theta)
logL = -log(2 * pi * sigma * sigma) * nDims / 2.0 - sum(
(theta / sigma)**2) / 2 + log(2) * nDims
return logL
def prior(cube):
""" prior mapping [0,1] -> [-1, 1]"""
return -1 + 2 * cube
#run_pyBAMBI(loglikelihood, prior, nDims, nested_sampler='multinest', nlive=500)
run_pyBAMBI(loglikelihood, prior, nDims, nested_sampler='polychord', nlive=500)
from pybambi import run_pyBAMBI
from numpy import pi, log
nDims = 3
def loglikelihood(theta):
""" Spherical Gaussian Likelihood """
sigma = 0.1
nDims = len(theta)
logL = -sum((theta / sigma)**2) / 2
logL += -log(2 * pi * sigma * sigma) * nDims / 2.0 + log(2) * nDims
return logL
def prior(cube):
""" prior mapping [0,1] -> [-1, 1]"""
return -1 + 2 * cube
run_pyBAMBI(loglikelihood,
prior,
nDims,
nested_sampler='multinest',
nlive=500,
learner='keras')