def __iter__(self):
"""
Yield Realization tuples. Notice that the weight is homogeneous when
sampling is enabled, since it is accounted for in the sampling
procedure.
"""
if self.num_samples:
# random sampling of the logic tree
probs = random((self.num_samples, len(self.bsetdict)),
self.seed, self.sampling_method)
ordinal = 0
for branches in self.root_branchset.sample(
probs, self.sampling_method):
name = branches[0].value
smlt_path_ids = [br.branch_id for br in branches]
if self.sampling_method.startswith('early_'):
weight = 1. / self.num_samples # already accounted
elif self.sampling_method.startswith('late_'):
weight = numpy.prod([br.weight for br in branches])
else:
raise NotImplementedError(self.sampling_method)
yield Realization(name, weight, ordinal, tuple(smlt_path_ids))
ordinal += 1
else: # full enumeration
ordinal = 0
for weight, branches in self.root_branchset.enumerate_paths():
name = branches[0].value # source model name
branch_ids = [branch.branch_id for branch in branches]
yield Realization(name, weight, ordinal, tuple(branch_ids))
ordinal += 1
def sample(self, n, seed, sampling_method='early_weights'):
"""
:param n: number of samples
:param seed: random seed
:param sampling_method: by default 'early_weights'
:returns: n Realization objects
"""
m = len(self.values) # number of TRTs
probs = lt.random((n, m), seed, sampling_method)
brlists = [
lt.sample([b for b in self.branches if b.trt == trt], probs[:, i],
sampling_method) for i, trt in enumerate(self.values)
]
rlzs = []
for i in range(n):
weight = 1
lt_path = []
lt_uid = []
value = []
for brlist in brlists: # there is branch list for each TRT
branch = brlist[i]
lt_path.append(branch.id)
lt_uid.append(branch.id if branch.effective else '@')
weight *= branch.weight
value.append(branch.gsim)
rlz = lt.Realization(tuple(value), weight, i, tuple(lt_uid))
rlzs.append(rlz)
return rlzs