def __init__(self,
system,
nreplicas,
mc_runner,
minima,
energy_accuracy,
compare_structures=None,
mindist=None,
copy_minima=True,
config_tests=None,
minimizer=None,
debug=True,
**kwargs):
super(NestedSamplingSAExact, self).__init__(system, nreplicas,
mc_runner, **kwargs)
self.debug = debug
if copy_minima:
self.minima = [_UnboundMinimum(m) for m in minima]
else:
self.minima = minima
if self.verbose:
self._check_minima()
self.compare_structures = compare_structures
if compare_structures is None:
try:
self.compare_structures = self.system.get_compare_exact()
except NotImplementedError or AttributeError:
pass
self.mindist = mindist
if mindist is None:
try:
self.mindist = self.system.get_mindist()
except NotImplementedError or AttributeError:
pass
self.config_tests = config_tests
if config_tests is None:
try:
self.config_tests = self.system.get_config_tests()
except NotImplementedError or AttributeError:
pass
self.minimizer = minimizer
if self.minimizer is None:
self.minimizer = self.system.get_minimizer()
self.minima_searcher = _MinimaSearcher(self.minima, energy_accuracy,
self.compare_structures)
self.sa_sampler = SASampler(self.minima, self.system.k)
self.count_sampled_minima = 0
def setUp(self):
self.natoms = 6
self.system = LJCluster(self.natoms)
# create a database
self.database = self.system.create_database()
# add some minima to the database
bh = self.system.get_basinhopping(self.database, outstream=None)
while self.database.number_of_minima() < 2:
bh.run(1)
get_thermodynamic_information(self.system, self.database)
get_all_normalmodes(self.system, self.database)
self.ndof = self.natoms * 3 - 6
self.sampler = SASampler(self.database.minima(), self.ndof)
def __init__(self,
system,
nreplicas,
mc_runner,
minima,
minprob=None,
energy_offset=None,
**kwargs):
super(NestedSamplingSA, self).__init__(system, nreplicas, mc_runner,
**kwargs)
self.minima = minima
self.bh_sampler = SASampler(self.minima, self.system.k)
if minprob is None:
raise ValueError("minprob cannot be None")
self.minprob = minprob
if energy_offset is None:
self.energy_offset = 2.5
else:
self.energy_offset = energy_offset
self.count_sampled_minima = 0
