def test_reduce_source_model(self):
case2 = os.path.dirname(case_2.__file__)
smlt = os.path.join(case2, 'source_model_logic_tree.xml')
found, total = readinput.reduce_source_model(smlt, [], remove=False)
self.assertEqual(found, 0)
found, total = readinput.reduce_source_model(smlt, {}, remove=False)
self.assertEqual(found, 0)
def reduce_sm(calc_id):
"""
Reduce the source model of the given (pre)calculation by discarding all
sources that do not contribute to the hazard.
"""
with datastore.read(calc_id) as dstore:
oqparam = dstore['oqparam']
info = dstore['source_info'].value
ok = info['weight'] > 0
source_ids = set(info[ok]['source_id'])
with performance.Monitor() as mon:
readinput.reduce_source_model(
oqparam.inputs['source_model_logic_tree'], source_ids)
print(mon)
def reduce_sm(calc_id):
"""
Reduce the source model of the given (pre)calculation by discarding all
sources that do not contribute to the hazard.
"""
with datastore.read(calc_id) as dstore:
oqparam = dstore['oqparam']
info = dstore['source_info'].value
ok = info['weight'] > 0
source_ids = set(info[ok]['source_id'])
with performance.Monitor() as mon:
readinput.reduce_source_model(
oqparam.inputs['source_model_logic_tree'], source_ids)
print(mon)
def reduce_sm(calc_id):
"""
Reduce the source model of the given (pre)calculation by discarding all
sources that do not contribute to the hazard.
"""
with datastore.read(calc_id) as dstore:
oqparam = dstore['oqparam']
info = dstore['source_info'][()]
bad_ids = set(info[info['eff_ruptures'] == 0]['source_id'])
if len(bad_ids) == 0:
logging.warning('All sources are relevant, nothing to remove')
return
ok = info['eff_ruptures'] > 0
if ok.sum() == 0:
raise RuntimeError('All sources were filtered away!')
ok_ids = general.group_array(info[ok][['source_id', 'code']], 'source_id')
with performance.Monitor() as mon:
good, total = readinput.reduce_source_model(
oqparam.inputs['source_model_logic_tree'], ok_ids)
logging.info('Removed %d/%d sources', total - good, good)
srcs, cnts = np.unique(info[['source_id', 'code']], return_counts=True)
dupl = srcs[cnts > 1]
if bad_ids & set(dict(dupl)):
logging.info('There were duplicated sources %s', dupl)
print(mon)
def main(calc_id: int):
"""
Reduce the source model of the given (pre)calculation by discarding all
sources that do not contribute to the hazard.
"""
if os.environ.get('OQ_DISTRIBUTE') not in ('no', 'processpool'):
os.environ['OQ_DISTRIBUTE'] = 'processpool'
with datastore.read(calc_id) as dstore:
oqparam = dstore['oqparam']
info = dstore['source_info'][()]
src_ids = info['source_id']
num_ids = len(src_ids)
bad_ids = info[info['eff_ruptures'] == 0]['source_id']
logging.info('Found %d far away sources', len(bad_ids))
bad_ids = set(
src_id.split(';')[0] for src_id in python3compat.decode(bad_ids))
bad_dupl = bad_ids & get_dupl(python3compat.decode(src_ids))
if bad_dupl:
logging.info('Duplicates %s not removed' % bad_dupl)
ok = info['eff_ruptures'] > 0
if ok.sum() == 0:
raise RuntimeError('All sources were filtered away!')
ok_ids = general.group_array(info[ok][['source_id', 'code']], 'source_id')
with performance.Monitor() as mon:
good, total = readinput.reduce_source_model(
oqparam.inputs['source_model_logic_tree'], ok_ids)
logging.info('Removed %d/%d sources', total - good, num_ids)
print(mon)
def reduce_sm(calc_id):
"""
Reduce the source model of the given (pre)calculation by discarding all
sources that do not contribute to the hazard.
"""
with datastore.read(calc_id) as dstore:
oqparam = dstore['oqparam']
info = dstore['source_info'][()]
ok = info['calc_time'] > 0
source_ids = set(info[ok]['source_id'])
if ok.sum() == 0:
raise RuntimeError('All sources were filtered away!')
with performance.Monitor() as mon:
readinput.reduce_source_model(
oqparam.inputs['source_model_logic_tree'], source_ids)
print(mon)
def reduce_sm(calc_id):
"""
Reduce the source model of the given (pre)calculation by discarding all
sources that do not contribute to the hazard.
"""
with datastore.read(calc_id) as dstore:
oqparam = dstore['oqparam']
info = dstore['source_info'][()]
num_ids = len(info['source_id'])
bad_ids = set(info[info['eff_ruptures'] == 0]['source_id'])
if len(bad_ids) == 0:
dupl = info[info['multiplicity'] > 1]['source_id']
if len(dupl) == 0:
logging.info('Nothing to remove')
else:
logging.info('Nothing to remove, but there are duplicated source '
'IDs that could prevent the removal: %s' % dupl)
return
logging.info('Found %d far away sources', len(bad_ids))
ok = info['eff_ruptures'] > 0
if ok.sum() == 0:
raise RuntimeError('All sources were filtered away!')
ok_ids = general.group_array(info[ok][['source_id', 'code']], 'source_id')
with performance.Monitor() as mon:
good, total = readinput.reduce_source_model(
oqparam.inputs['source_model_logic_tree'], ok_ids)
logging.info('Removed %d/%d sources', total - good, num_ids)
print(mon)
def test_reduce_source_model(self):
case2 = os.path.dirname(case_2.__file__)
smlt = os.path.join(case2, 'source_model_logic_tree.xml')
readinput.reduce_source_model(smlt, [])
def test_reduce_source_model(self):
case2 = os.path.dirname(case_2.__file__)
smlt = os.path.join(case2, 'source_model_logic_tree.xml')
readinput.reduce_source_model(smlt, [], False)
