class RuptureData(object):
"""
Container for information about the ruptures of a given
tectonic region type.
"""
def __init__(self, trt, gsims):
self.trt = trt
self.cmaker = ContextMaker(gsims)
self.params = sorted(self.cmaker.REQUIRES_RUPTURE_PARAMETERS -
set('mag strike dip rake hypo_depth'.split()))
self.dt = numpy.dtype(
[('rup_id', U32), ('multiplicity', U16), ('eidx', U32),
('numsites', U32), ('occurrence_rate', F64), ('mag', F32),
('lon', F32), ('lat', F32), ('depth', F32), ('strike', F32),
('dip', F32), ('rake', F32), ('boundary', hdf5.vstr)] +
[(param, F32) for param in self.params])
def to_array(self, ebruptures):
"""
Convert a list of ebruptures into an array of dtype RuptureRata.dt
"""
data = []
for ebr in ebruptures:
rup = ebr.rupture
rc = self.cmaker.make_rupture_context(rup)
ruptparams = tuple(getattr(rc, param) for param in self.params)
point = rup.surface.get_middle_point()
multi_lons, multi_lats = rup.surface.get_surface_boundaries()
bounds = ','.join('((%s))' %
','.join('%.5f %.5f' % (lon, lat)
for lon, lat in zip(lons, lats))
for lons, lats in zip(multi_lons, multi_lats))
try:
rate = ebr.rupture.occurrence_rate
except AttributeError: # for nonparametric sources
rate = numpy.nan
data.append((ebr.serial, ebr.multiplicity, ebr.eidx1,
len(ebr.sids), rate, rup.mag, point.x, point.y,
point.z, rup.surface.get_strike(),
rup.surface.get_dip(), rup.rake,
'MULTIPOLYGON(%s)' % decode(bounds)) + ruptparams)
return numpy.array(data, self.dt)
class RuptureData(object):
"""
Container for information about the ruptures of a given
tectonic region type.
"""
def __init__(self, trt, gsims):
self.trt = trt
self.cmaker = ContextMaker(gsims)
self.params = sorted(self.cmaker.REQUIRES_RUPTURE_PARAMETERS -
set('mag strike dip rake hypo_depth'.split()))
self.dt = numpy.dtype([
('rupserial', U32), ('multiplicity', U16),
('numsites', U32), ('occurrence_rate', F64),
('mag', F64), ('lon', F32), ('lat', F32), ('depth', F32),
('strike', F64), ('dip', F64), ('rake', F64),
('boundary', hdf5.vstr)] + [(param, F64) for param in self.params])
def to_array(self, ebruptures):
data = []
for ebr in ebruptures:
rup = ebr.rupture
rc = self.cmaker.make_rupture_context(rup)
ruptparams = tuple(getattr(rc, param) for param in self.params)
point = rup.surface.get_middle_point()
multi_lons, multi_lats = rup.surface.get_surface_boundaries()
boundary = ','.join('((%s))' % ','.join(
'%.5f %.5f' % (lon, lat) for lon, lat in zip(lons, lats))
for lons, lats in zip(multi_lons, multi_lats))
try:
rate = ebr.rupture.occurrence_rate
except AttributeError: # for nonparametric sources
rate = numpy.nan
data.append((ebr.serial, ebr.multiplicity, len(ebr.sids),
rate, rup.mag, point.x, point.y, point.z,
rup.surface.get_strike(), rup.surface.get_dip(),
rup.rake, decode(boundary)) + ruptparams)
return numpy.array(data, self.dt)
def compute_ruptures(sources, sitecol, siteidx, rlzs_assoc, monitor):
"""
:param sources:
List of commonlib.source.Source tuples
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param siteidx:
always equal to 0
:param rlzs_assoc:
a :class:`openquake.commonlib.source.RlzsAssoc` instance
:param monitor:
monitor instance
:returns:
a dictionary trt_model_id -> [Rupture instances]
"""
assert siteidx == 0, (
'siteidx can be nonzero only for the classical_tiling calculations: '
'tiling with the EventBasedRuptureCalculator is an error')
# NB: by construction each block is a non-empty list with
# sources of the same trt_model_id
trt_model_id = sources[0].trt_model_id
oq = monitor.oqparam
trt = sources[0].tectonic_region_type
max_dist = oq.maximum_distance[trt]
cmaker = ContextMaker(rlzs_assoc.gsims_by_trt_id[trt_model_id])
params = cmaker.REQUIRES_RUPTURE_PARAMETERS
rup_data_dt = numpy.dtype([('rupserial', U32), ('multiplicity',
U16), ('numsites', U32)] +
[(param, F32) for param in params])
eb_ruptures = []
rup_data = []
calc_times = []
rup_mon = monitor('filtering ruptures', measuremem=False)
num_samples = rlzs_assoc.samples[trt_model_id]
# Compute and save stochastic event sets
for src in sources:
t0 = time.time()
s_sites = src.filter_sites_by_distance_to_source(max_dist, sitecol)
if s_sites is None:
continue
rupture_filter = functools.partial(filter_sites_by_distance_to_rupture,
integration_distance=max_dist,
sites=s_sites)
num_occ_by_rup = sample_ruptures(src, oq.ses_per_logic_tree_path,
num_samples, rlzs_assoc.seed)
# NB: the number of occurrences is very low, << 1, so it is
# more efficient to filter only the ruptures that occur, i.e.
# to call sample_ruptures *before* the filtering
for ebr in build_eb_ruptures(src, num_occ_by_rup, rupture_filter,
oq.random_seed, rup_mon):
nsites = len(ebr.indices)
rc = cmaker.make_rupture_context(ebr.rupture)
ruptparams = tuple(getattr(rc, param) for param in params)
rup_data.append((ebr.serial, len(ebr.etags), nsites) + ruptparams)
eb_ruptures.append(ebr)
dt = time.time() - t0
calc_times.append((src.id, dt))
res = AccumDict({trt_model_id: eb_ruptures})
res.calc_times = calc_times
res.rup_data = numpy.array(rup_data, rup_data_dt)
res.trt = trt
return res
def compute_ruptures(sources, sitecol, siteidx, rlzs_assoc, monitor):
"""
:param sources:
List of commonlib.source.Source tuples
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param siteidx:
always equal to 0
:param rlzs_assoc:
a :class:`openquake.commonlib.source.RlzsAssoc` instance
:param monitor:
monitor instance
:returns:
a dictionary src_group_id -> [Rupture instances]
"""
assert siteidx == 0, (
'siteidx can be nonzero only for the classical_tiling calculations: '
'tiling with the EventBasedRuptureCalculator is an error')
# NB: by construction each block is a non-empty list with
# sources of the same src_group_id
src_group_id = sources[0].src_group_id
oq = monitor.oqparam
trt = sources[0].tectonic_region_type
max_dist = oq.maximum_distance[trt]
cmaker = ContextMaker(rlzs_assoc.gsims_by_grp_id[src_group_id])
params = sorted(cmaker.REQUIRES_RUPTURE_PARAMETERS)
rup_data_dt = numpy.dtype(
[('rupserial', U32), ('multiplicity', U16),
('numsites', U32), ('occurrence_rate', F32)] + [
(param, F32) for param in params])
eb_ruptures = []
rup_data = []
calc_times = []
rup_mon = monitor('filtering ruptures', measuremem=False)
num_samples = rlzs_assoc.samples[src_group_id]
# Compute and save stochastic event sets
for src in sources:
t0 = time.time()
s_sites = src.filter_sites_by_distance_to_source(max_dist, sitecol)
if s_sites is None:
continue
rupture_filter = functools.partial(
filter_sites_by_distance_to_rupture,
integration_distance=max_dist, sites=s_sites)
num_occ_by_rup = sample_ruptures(
src, oq.ses_per_logic_tree_path, num_samples,
rlzs_assoc.seed)
# NB: the number of occurrences is very low, << 1, so it is
# more efficient to filter only the ruptures that occur, i.e.
# to call sample_ruptures *before* the filtering
for ebr in build_eb_ruptures(
src, num_occ_by_rup, rupture_filter, oq.random_seed, rup_mon):
nsites = len(ebr.indices)
try:
rate = ebr.rupture.occurrence_rate
except AttributeError: # for nonparametric sources
rate = numpy.nan
rc = cmaker.make_rupture_context(ebr.rupture)
ruptparams = tuple(getattr(rc, param) for param in params)
rup_data.append((ebr.serial, len(ebr.etags), nsites, rate) +
ruptparams)
eb_ruptures.append(ebr)
dt = time.time() - t0
calc_times.append((src.id, dt))
res = AccumDict({src_group_id: eb_ruptures})
res.calc_times = calc_times
res.rup_data = numpy.array(rup_data, rup_data_dt)
res.trt = trt
return res
def compute_ruptures(sources, sitecol, gsims, monitor):
"""
:param sources:
List of commonlib.source.Source tuples
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:param gsims:
a list of GSIMs for the current tectonic region model
:param monitor:
monitor instance
:returns:
a dictionary src_group_id -> [Rupture instances]
"""
# NB: by construction each block is a non-empty list with
# sources of the same src_group_id
src_group_id = sources[0].src_group_id
trt = sources[0].tectonic_region_type
max_dist = monitor.maximum_distance[trt]
cmaker = ContextMaker(gsims)
params = sorted(cmaker.REQUIRES_RUPTURE_PARAMETERS)
rup_data_dt = numpy.dtype(
[('rupserial', U32), ('multiplicity', U16),
('numsites', U32), ('occurrence_rate', F64)] + [
(param, F64) for param in params])
eb_ruptures = []
rup_data = []
calc_times = []
rup_mon = monitor('filtering ruptures', measuremem=False)
num_samples = monitor.samples
num_events = 0
# Compute and save stochastic event sets
for src in sources:
t0 = time.time()
s_sites = src.filter_sites_by_distance_to_source(max_dist, sitecol)
if s_sites is None:
continue
rupture_filter = functools.partial(
filter_sites_by_distance_to_rupture,
integration_distance=max_dist, sites=s_sites)
num_occ_by_rup = sample_ruptures(
src, monitor.ses_per_logic_tree_path, num_samples,
monitor.seed)
# NB: the number of occurrences is very low, << 1, so it is
# more efficient to filter only the ruptures that occur, i.e.
# to call sample_ruptures *before* the filtering
for ebr in build_eb_ruptures(
src, num_occ_by_rup, rupture_filter, monitor.seed, rup_mon):
nsites = len(ebr.indices)
try:
rate = ebr.rupture.occurrence_rate
except AttributeError: # for nonparametric sources
rate = numpy.nan
rc = cmaker.make_rupture_context(ebr.rupture)
ruptparams = tuple(getattr(rc, param) for param in params)
rup_data.append((ebr.serial, ebr.multiplicity, nsites, rate) +
ruptparams)
eb_ruptures.append(ebr)
num_events += ebr.multiplicity
dt = time.time() - t0
calc_times.append((src.id, dt))
res = AccumDict({src_group_id: eb_ruptures})
res.num_events = num_events
res.calc_times = calc_times
res.rup_data = numpy.array(rup_data, rup_data_dt)
res.trt = trt
return res
