def __call__(self, ltmodel, apply_unc, fname, fileno, monitor):
fname_hits = collections.Counter() # fname -> number of calls
mags = set()
src_groups = []
[sm] = nrml.read_source_models([fname], self.converter, monitor)
newsm = self.makesm(fname, sm, apply_unc)
fname_hits[fname] += 1
for sg in newsm:
# sample a source for each group
if os.environ.get('OQ_SAMPLE_SOURCES'):
sg.sources = random_filtered_sources(
sg.sources, self.srcfilter, sg.id)
sg.info = numpy.zeros(len(sg), source_info_dt)
for i, src in enumerate(sg):
if hasattr(src, 'data'): # nonparametric
srcmags = ['%.3f' % item[0].mag for item in src.data]
else:
srcmags = ['%.3f' % item[0] for item in
src.get_annual_occurrence_rates()]
mags.update(srcmags)
dic = {k: v for k, v in vars(src).items()
if k != 'id' and k != 'src_group_id'}
src.checksum = zlib.adler32(
pickle.dumps(dic, pickle.HIGHEST_PROTOCOL))
sg.info[i] = (ltmodel.ordinal, 0, src.source_id,
src.code, src.num_ruptures, 0, 0, 0,
src.checksum, src.wkt())
src_groups.append(sg)
return dict(fname_hits=fname_hits, changes=newsm.changes,
src_groups=src_groups, mags=mags,
ordinal=ltmodel.ordinal, fileno=fileno)
def read_source_model(fname, converter, monitor):
"""
:param fname: path to a source model XML file
:param converter: SourceConverter
:param monitor: a Monitor instance
:returns: a SourceModel instance
"""
[sm] = nrml.read_source_models([fname], converter)
return {fname: sm}
def read_source_model(fname, converter, srcfilter, monitor):
"""
:param fname: path to a source model XML file
:param converter: SourceConverter
:param srcfilter: None unless OQ_SAMPLE_SOURCES is set
:param monitor: a Monitor instance
:returns: a SourceModel instance
"""
[sm] = nrml.read_source_models([fname], converter)
if srcfilter: # if OQ_SAMPLE_SOURCES is set sample the close sources
for i, sg in enumerate(sm.src_groups):
sg.sources = random_filtered_sources(sg.sources, srcfilter, i)
return {fname: sm}
def read_input(hparams, **extra):
"""
:param hparams: a dictionary of hazard parameters
:returns: an Input namedtuple (groups, sitecol, gsim_lt, cmakerdict)
The dictionary must contain the keys
- "maximum_distance"
- "imtls"
- "source_model_file" or "rupture_model_file"
- "sites" or "site_model_file"
- "gsim" or "gsim_logic_tree_file"
Moreover:
- if "source_model_file" is given, then "investigation_time" is mandatory
- if "rupture_model_file" is given, the "number_of_ground_motion_fields"
and "ses_seed" are mandatory
- if there is an area source, then "area_source_discretization" is needed
- if "site_model_file" is missing, then global site parameters are needed
The optional keys include
- "rupture_mesh_spacing" (default 5.)
- "complex_fault_mesh_spacing" (default rupture_mesh_spacing)
- "width_of_mfd_bin" (default 1.)
- "minimum_magnitude"
- "discard_trts" (default "")
- "number_of_logic_tree_samples" (default 0)
- "ses_per_logic_tree_path" (default 1)
"""
if isinstance(hparams, str):
hparams = read_hparams(hparams)
if extra:
hparams = hparams.copy()
hparams.update(extra)
assert 'imts' in hparams or 'imtls' in hparams
assert isinstance(hparams['maximum_distance'], IntegrationDistance)
smfname = hparams.get('source_model_file')
if smfname: # nonscenario
itime = hparams['investigation_time']
else:
itime = 50. # ignored in scenario
rmfname = hparams.get('rupture_model_file')
if rmfname:
ngmfs = hparams["number_of_ground_motion_fields"]
ses_seed = hparams["ses_seed"]
converter = sourceconverter.SourceConverter(
itime,
hparams.get('rupture_mesh_spacing', 5.),
hparams.get('complex_fault_mesh_spacing'),
hparams.get('width_of_mfd_bin', 1.0),
hparams.get('area_source_discretization'),
hparams.get('minimum_magnitude', {'default': 0}),
hparams.get('source_id'),
discard_trts=hparams.get('discard_trts', ''))
if smfname:
[sm] = nrml.read_source_models([smfname], converter)
groups = sm.src_groups
elif rmfname:
ebrs = _get_ebruptures(rmfname, converter, ses_seed)
groups = _rupture_groups(ebrs)
else:
raise KeyError('Missing source_model_file or rupture_file')
trts = set(grp.trt for grp in groups)
if 'gsim' in hparams:
gslt = gsim_lt.GsimLogicTree.from_(hparams['gsim'])
else:
gslt = gsim_lt.GsimLogicTree(hparams['gsim_logic_tree_file'], trts)
# fix source attributes
idx = 0
num_rlzs = gslt.get_num_paths()
for grp_id, sg in enumerate(groups):
assert len(sg) # sanity check
for src in sg:
src.id = idx
src.grp_id = grp_id
src.trt_smr = grp_id
src.samples = num_rlzs
idx += 1
cmakerdict = {} # trt => cmaker
start = 0
n = hparams.get('number_of_logic_tree_samples', 0)
s = hparams.get('random_seed', 42)
for trt, rlzs_by_gsim in gslt.get_rlzs_by_gsim_trt(n, s).items():
cmakerdict[trt] = contexts.ContextMaker(trt, rlzs_by_gsim, hparams)
cmakerdict[trt].start = start
start += len(rlzs_by_gsim)
if rmfname:
# for instance for 2 TRTs with 5x2 GSIMs and ngmfs=10, then the
# number of occupation is 100 for each rupture, for a total
# of 200 events, see scenario/case_13
nrlzs = gslt.get_num_paths()
for grp in groups:
for ebr in grp:
ebr.n_occ = ngmfs * nrlzs
sitecol = _get_sitecol(hparams, gslt.req_site_params)
return Input(groups, sitecol, gslt, cmakerdict)
