def save_events(self, rup_array):
"""
:param rup_array: an array of ruptures with fields grp_id
:returns: a list of RuptureGetters
"""
# this is very fast compared to saving the ruptures
eids = rupture.get_eids(
rup_array, self.samples_by_grp, self.num_rlzs_by_grp)
self.check_overflow() # check the number of events
events = numpy.zeros(len(eids), rupture.events_dt)
# when computing the events all ruptures must be considered,
# including the ones far away that will be discarded later on
rgetters = self.gen_rupture_getters()
# build the associations eid -> rlz in parallel
smap = parallel.Starmap(RuptureGetter.get_eid_rlz,
((rgetter,) for rgetter in rgetters),
self.monitor('get_eid_rlz'),
progress=logging.debug)
i = 0
for eid_rlz in smap: # 30 million of events associated in 1 minute!
for er in eid_rlz:
events[i] = er
i += 1
if i >= TWO32:
raise ValueError('There are more than %d events!' % i)
events.sort(order='id') # fast too
n_unique_events = len(numpy.unique(events['id']))
assert n_unique_events == len(events), (n_unique_events, len(events))
self.datastore['events'] = events
def save_events(self, rup_array):
"""
:param rup_array: an array of ruptures with fields grp_id
:returns: a list of RuptureGetters
"""
# this is very fast compared to saving the ruptures
eids = rupture.get_eids(
rup_array, self.samples_by_grp, self.num_rlzs_by_grp)
self.check_overflow() # check the number of events
events = numpy.zeros(len(eids), rupture.events_dt)
# when computing the events all ruptures must be considered,
# including the ones far away that will be discarded later on
rgetters = self.gen_rupture_getters()
# build the associations eid -> rlz in parallel
smap = parallel.Starmap(RuptureGetter.get_eid_rlz,
((rgetter,) for rgetter in rgetters),
self.monitor('get_eid_rlz'),
progress=logging.debug)
i = 0
for eid_rlz in smap: # 30 million of events associated in 1 minute!
for er in eid_rlz:
events[i] = er
i += 1
events.sort(order=['rlz', 'eid']) # fast too
n_unique_events = len(numpy.unique(events['eid']))
assert n_unique_events == len(events), (n_unique_events, len(events))
self.datastore['events'] = events
indices = numpy.zeros((self.R, 2), U32)
for r, [startstop] in get_indices(events['rlz']).items():
indices[r] = startstop
self.datastore.set_attrs('events', indices=indices)
def save_events(self, rup_array):
"""
:param rup_array: an array of ruptures with fields grp_id
:returns: a list of RuptureGetters
"""
# this is very fast compared to saving the ruptures
eids = rupture.get_eids(rup_array, self.samples_by_grp,
self.num_rlzs_by_grp)
self.E = len(eids)
self.check_overflow() # check the number of events
events = numpy.zeros(len(eids), rupture.events_dt)
events['eid'] = eids
self.eid2idx = eid2idx = dict(zip(events['eid'], range(self.E)))
rgetters = self.get_rupture_getters()
# build the associations eid -> rlz in parallel
smap = parallel.Starmap(RuptureGetter.get_eid_rlz,
((rgetter, ) for rgetter in rgetters),
self.monitor('get_eid_rlz'),
progress=logging.debug)
for eid_rlz in smap:
# fast: 30 million of events associated in 1 minute
for eid, rlz in eid_rlz:
events[eid2idx[eid]]['rlz'] = rlz
self.datastore['events'] = events # fast too
return rgetters
def save_events(self, rup_array):
"""
:param rup_array: an array of ruptures with fields grp_id
:returns: a list of RuptureGetters
"""
# this is very fast compared to saving the ruptures
eids = rupture.get_eids(rup_array, self.samples_by_grp,
self.num_rlzs_by_grp)
self.check_overflow() # check the number of events
events = numpy.zeros(len(eids), rupture.events_dt)
# when computing the events all ruptures must be considered,
# including the ones far away that will be discarded later on
rgetters = gen_rgetters(self.datastore)
# build the associations eid -> rlz sequentially or in parallel
# this is very fast: I saw 30 million events associated in 1 minute!
logging.info('Building assocs event_id -> rlz_id for {:_d} events'
' and {:_d} ruptures'.format(len(events), len(rup_array)))
if len(events) < 1E5:
it = map(RuptureGetter.get_eid_rlz, rgetters)
else:
# parallel composite array with the associations eid->rlz
it = parallel.Starmap(RuptureGetter.get_eid_rlz,
((rgetter, ) for rgetter in rgetters),
progress=logging.debug,
h5=self.datastore.hdf5)
i = 0
for eid_rlz in it:
for er in eid_rlz:
events[i] = er
i += 1
if i >= TWO32:
raise ValueError('There are more than %d events!' % i)
events.sort(order='rup_id') # fast too
# sanity check
n_unique_events = len(numpy.unique(events[['id', 'rup_id']]))
assert n_unique_events == len(events), (n_unique_events, len(events))
events['id'] = numpy.arange(len(events))
# set event year and event ses starting from 1
itime = int(self.oqparam.investigation_time)
nses = self.oqparam.ses_per_logic_tree_path
extra = numpy.zeros(len(events), [('year', U16), ('ses_id', U16)])
# seed for year and ses_id
numpy.random.seed(self.oqparam.ses_seed)
extra['year'] = numpy.random.choice(itime, len(events)) + 1
extra['ses_id'] = numpy.random.choice(nses, len(events)) + 1
self.datastore['events'] = util.compose_arrays(events, extra)
eindices = get_indices(events['rup_id'])
arr = numpy.array(list(eindices.values()))[:, 0, :]
self.datastore['ruptures']['e0'] = arr[:, 0]
self.datastore['ruptures']['e1'] = arr[:, 1]
def save_events(self, rup_array):
"""
:param rup_array: an array of ruptures with fields grp_id
:returns: a list of RuptureGetters
"""
# this is very fast compared to saving the ruptures
eids = rupture.get_eids(rup_array, self.samples_by_grp,
self.num_rlzs_by_grp)
self.check_overflow() # check the number of events
events = numpy.zeros(len(eids), rupture.events_dt)
# when computing the events all ruptures must be considered,
# including the ones far away that will be discarded later on
rgetters = self.gen_rupture_getters()
# build the associations eid -> rlz sequentially or in parallel
# this is very fast: I saw 30 million events associated in 1 minute!
logging.info(
'Building associations event_id -> rlz_id for %d events'
' and %d ruptures', len(events), len(rup_array))
if len(events) < 1E5:
it = map(RuptureGetter.get_eid_rlz, rgetters)
else:
it = parallel.Starmap(RuptureGetter.get_eid_rlz,
((rgetter, ) for rgetter in rgetters),
progress=logging.debug,
hdf5path=self.datastore.filename)
i = 0
for eid_rlz in it:
for er in eid_rlz:
events[i] = er
i += 1
if i >= TWO32:
raise ValueError('There are more than %d events!' % i)
events.sort(order='rup_id') # fast too
# sanity check
n_unique_events = len(numpy.unique(events[['id', 'rup_id']]))
assert n_unique_events == len(events), (n_unique_events, len(events))
events['id'] = numpy.arange(len(events))
self.datastore['events'] = events
eindices = get_indices(events['rup_id'])
arr = numpy.array(list(eindices.values()))[:, 0, :]
self.datastore['eslices'] = arr # shape (U, 2)