def post_execute(self, result):
"""
:param result:
a dictionary (trt_model_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
if oq.hazard_curves_from_gmfs:
ClassicalCalculator.post_execute.__func__(self, result)
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# use a different datastore
self.cl = ClassicalCalculator(oq, self.monitor)
self.cl.datastore.parent = self.datastore
result = self.cl.run(pre_execute=False, clean_up=False)
for imt in self.mean_curves.dtype.fields:
rdiff, index = max_rel_diff_index(self.cl.mean_curves[imt],
self.mean_curves[imt])
logging.warn(
'Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d', imt,
rdiff * 100, index)
def post_execute(self, result):
"""
:param result:
a dictionary (src_group_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
elif oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
dic = {}
for rlzi in result:
dic[rlzs[rlzi]] = array_of_curves(
result[rlzi], len(self.sitecol), oq.imtls)
self.save_curves(dic)
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# one could also set oq.number_of_logic_tree_samples = 0
self.cl = ClassicalCalculator(oq, self.monitor)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run()
for imt in self.mean_curves.dtype.fields:
rdiff, index = max_rel_diff_index(
self.cl.mean_curves[imt], self.mean_curves[imt])
logging.warn('Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d',
imt, rdiff * 100, index)
def test_case_7(self):
# 2 models x 3 GMPEs, 1000 samples * 10 SES
expected = [
'hazard_curve-mean.csv',
]
out = self.run_calc(case_7.__file__, 'job.ini', exports='csv')
aw = extract(self.calc.datastore, 'realizations')
dic = countby(aw.array, 'branch_path')
self.assertEqual(
{
b'b11~BA': 332, # w = .6 * .5 = .30
b'b11~CB': 169, # w = .6 * .3 = .18
b'b11~CY': 108, # w = .6 * .2 = .12
b'b12~BA': 193, # w = .4 * .5 = .20
b'b12~CB': 115, # w = .4 * .3 = .12
b'b12~CY': 83
}, # w = .4 * .2 = .08
dic)
fnames = out['hcurves', 'csv']
mean_eb = get_mean_curves(self.calc.datastore)
for exp, got in zip(expected, fnames):
self.assertEqualFiles('expected/%s' % exp, got)
mean_cl = get_mean_curves(self.calc.cl.datastore)
reldiff, _index = max_rel_diff_index(mean_cl, mean_eb, min_value=0.1)
self.assertLess(reldiff, 0.07)
def post_execute(self, result):
"""
:param result:
a dictionary (trt_model_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
elif oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
dic = {}
for rlzi in result:
dic[rlzs[rlzi]] = array_of_curves(result[rlzi],
len(self.sitecol), oq.imtls)
self.save_curves(dic)
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# one could also set oq.number_of_logic_tree_samples = 0
self.cl = ClassicalCalculator(oq, self.monitor)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(hazard_calculation_id=self.datastore.calc_id)
for imt in self.mean_curves.dtype.fields:
rdiff, index = max_rel_diff_index(self.cl.mean_curves[imt],
self.mean_curves[imt])
logging.warn(
'Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d', imt,
rdiff * 100, index)
def post_execute(self, result):
"""
Return a dictionary with the output files, i.e. gmfs (if any)
and hazard curves (if any).
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
if oq.hazard_curves_from_gmfs:
ClassicalCalculator.post_execute.__func__(self, result)
if oq.ground_motion_fields:
for (trt_id, gsim), gmf_by_tag in self.gmf_dict.items():
self.gmf_dict[trt_id, gsim] = {tag: gmf_by_tag[tag][gsim]
for tag in gmf_by_tag}
self.gmf_by_trt_gsim = self.gmf_dict
self.gmf_dict.clear()
if oq.mean_hazard_curves: # compute classical ones
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# use a different datastore
self.cl = ClassicalCalculator(oq, self.monitor)
# copy the relevant attributes
self.cl.composite_source_model = self.csm
self.cl.sitecol = self.sitecol
self.cl.rlzs_assoc = self.csm.get_rlzs_assoc()
result = self.cl.run(pre_execute=False, clean_up=False)
for imt in self.mean_curves.dtype.fields:
rdiff, index = max_rel_diff_index(
self.cl.mean_curves[imt], self.mean_curves[imt])
logging.warn('Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d',
imt, rdiff * 100, index)
def post_execute(self, result):
"""
:param result:
a dictionary (trt_model_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
if oq.hazard_curves_from_gmfs:
ClassicalCalculator.post_execute.__func__(self, result)
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# use a different datastore
self.cl = ClassicalCalculator(oq, self.monitor)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(hazard_calculation_id=self.datastore.calc_id)
for imt in self.mean_curves.dtype.fields:
rdiff, index = max_rel_diff_index(
self.cl.mean_curves[imt], self.mean_curves[imt])
logging.warn('Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d',
imt, rdiff * 100, index)
def post_execute(self, result):
"""
:param result:
a dictionary (trt_model_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
if oq.hazard_curves_from_gmfs:
ClassicalCalculator.post_execute.__func__(self, result)
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# use a different datastore
self.cl = ClassicalCalculator(oq, self.monitor)
# copy the relevant attributes
self.cl.composite_source_model = self.csm
self.cl.sitecol = self.sitecol.complete
self.cl.rlzs_assoc = self.csm.get_rlzs_assoc()
result = self.cl.run(pre_execute=False, clean_up=False)
for imt in self.mean_curves.dtype.fields:
rdiff, index = max_rel_diff_index(
self.cl.mean_curves[imt], self.mean_curves[imt])
logging.warn('Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d',
imt, rdiff * 100, index)
def post_execute(self, result):
"""
:param result:
a dictionary (src_group_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
elif oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
# save individual curves
if self.oqparam.individual_curves:
for i in sorted(result):
key = 'hcurves/rlz-%03d' % i
if result[i]:
self.datastore[key] = result[i]
else:
logging.info('Zero curves for %s', key)
# compute and save statistics; this is done in process
# we don't need to parallelize, since event based calculations
# involves a "small" number of sites (<= 65,536)
weights = (None if self.oqparam.number_of_logic_tree_samples
else [rlz.weight for rlz in rlzs])
pstats = PmapStats(self.oqparam.quantile_hazard_curves, weights)
for kind, stat in pstats.compute(
self.sitecol.sids, list(result.values())):
if kind == 'mean' and not self.oqparam.mean_hazard_curves:
continue
self.datastore['hcurves/' + kind] = stat
if ('gmf_data' in self.datastore and 'nbytes' not
in self.datastore['gmf_data'].attrs):
self.datastore.set_nbytes('gmf_data')
for sm_id in self.datastore['gmf_data']:
for rlzno in self.datastore['gmf_data/' + sm_id]:
self.datastore.set_nbytes(
'gmf_data/%s/%s' % (sm_id, rlzno))
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# one could also set oq.number_of_logic_tree_samples = 0
self.cl = ClassicalCalculator(oq, self.monitor)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(close=False)
cl_mean_curves = get_mean_curves(self.cl.datastore)
eb_mean_curves = get_mean_curves(self.datastore)
for imt in eb_mean_curves.dtype.names:
rdiff, index = util.max_rel_diff_index(
cl_mean_curves[imt], eb_mean_curves[imt])
logging.warn('Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d',
imt, rdiff * 100, index)
def post_execute(self, result):
"""
:param result:
a dictionary (src_group_id, gsim) -> haz_curves or an empty
dictionary if hazard_curves_from_gmfs is false
"""
oq = self.oqparam
if not oq.hazard_curves_from_gmfs and not oq.ground_motion_fields:
return
elif oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
# save individual curves
for i in sorted(result):
key = 'hcurves/rlz-%03d' % i
if result[i]:
self.datastore[key] = result[i]
else:
self.datastore[key] = ProbabilityMap(oq.imtls.array.size)
logging.info('Zero curves for %s', key)
# compute and save statistics; this is done in process
# we don't need to parallelize, since event based calculations
# involves a "small" number of sites (<= 65,536)
weights = [rlz.weight for rlz in rlzs]
hstats = self.oqparam.hazard_stats()
if len(hstats) and len(rlzs) > 1:
for kind, stat in hstats:
pmap = compute_pmap_stats(result.values(), [stat], weights)
self.datastore['hcurves/' + kind] = pmap
if self.datastore.parent:
self.datastore.parent.open()
if 'gmf_data' in self.datastore:
self.save_gmf_bytes()
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# one could also set oq.number_of_logic_tree_samples = 0
self.cl = ClassicalCalculator(oq, self.monitor('classical'))
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(close=False)
cl_mean_curves = get_mean_curves(self.cl.datastore)
eb_mean_curves = get_mean_curves(self.datastore)
for imt in eb_mean_curves.dtype.names:
rdiff, index = util.max_rel_diff_index(cl_mean_curves[imt],
eb_mean_curves[imt])
logging.warn(
'Relative difference with the classical '
'mean curves for IMT=%s: %d%% at site index %d', imt,
rdiff * 100, index)
def test_case_7(self):
# 2 models x 3 GMPEs, 100 samples * 20 SES
expected = [
'hazard_curve-mean.csv',
]
out = self.run_calc(case_7.__file__, 'job.ini', exports='csv')
fnames = out['hcurves', 'csv']
mean_eb = get_mean_curves(self.calc.datastore)
for exp, got in zip(expected, fnames):
self.assertEqualFiles('expected/%s' % exp, got)
mean_cl = get_mean_curves(self.calc.cl.datastore)
reldiff, _index = max_rel_diff_index(mean_cl, mean_eb, min_value=0.1)
self.assertLess(reldiff, 0.10)
def test_case_7(self):
# 2 models x 3 GMPEs, 100 samples * 20 SES
expected = [
'hazard_curve-mean.csv',
]
out = self.run_calc(case_7.__file__, 'job.ini', exports='csv')
fnames = out['hcurves', 'csv']
mean_eb = get_mean_curves(self.calc.datastore)
for exp, got in zip(expected, fnames):
self.assertEqualFiles('expected/%s' % exp, got)
mean_cl = get_mean_curves(self.calc.cl.datastore)
reldiff, _index = max_rel_diff_index(
mean_cl, mean_eb, min_value=0.1)
self.assertLess(reldiff, 0.10)
def test_case_7(self):
# 2 models x 3 GMPEs, 100 samples * 10 SES
expected = [
'hazard_curve-mean.csv',
'quantile_curve-0.1.csv',
'quantile_curve-0.9.csv',
]
out = self.run_calc(case_7.__file__, 'job.ini', exports='csv')
fnames = out['hcurves', 'csv']
mean_eb = self.calc.mean_curves
for exp, got in zip(expected, fnames):
self.assertEqualFiles('expected/%s' % exp, got)
mean_cl = self.calc.cl.mean_curves
for imt in mean_cl.dtype.fields:
reldiff, _index = max_rel_diff_index(
mean_cl[imt], mean_eb[imt], min_value=0.1)
self.assertLess(reldiff, 0.41)
def test_case_7(self):
# 2 models x 3 GMPEs, 100 samples * 10 SES
expected = [
'hazard_curve-mean.csv',
'quantile_curve-0.1.csv',
'quantile_curve-0.9.csv',
]
out = self.run_calc(case_7.__file__, 'job.ini', exports='csv')
fnames = out['hcurves', 'csv']
mean_eb = self.calc.mean_curves
for exp, got in zip(expected, fnames):
self.assertEqualFiles('expected/%s' % exp, got)
mean_cl = self.calc.cl.mean_curves
for imt in mean_cl.dtype.fields:
reldiff, _index = max_rel_diff_index(
mean_cl[imt], mean_eb[imt], min_value=0.1)
self.assertLess(reldiff, 0.41)
def test_case_7(self):
# 2 models x 3 GMPEs, 10 samples * 40 SES
expected = [
'hazard_curve-mean.csv',
'quantile_curve-0.1.csv',
'quantile_curve-0.9.csv',
]
out = self.run_calc(case_7.__file__, 'job.ini', exports='csv')
fnames = out['hcurves', 'csv']
mean_eb = get_mean_curves(self.calc.datastore)
for exp, got in zip(expected, fnames):
self.assertEqualFiles('expected/%s' % exp, got)
mean_cl = get_mean_curves(self.calc.cl.datastore)
for imt in mean_cl.dtype.fields:
reldiff, _index = max_rel_diff_index(mean_cl[imt],
mean_eb[imt],
min_value=0.1)
self.assertLess(reldiff, 0.20)
exp = self.calc.datastore.get_attr('events', 'max_gmf_size')
self.assertEqual(exp, 375496)
def post_execute(self, result):
"""
Save the SES collection
"""
oq = self.oqparam
N = len(self.sitecol.complete)
L = len(oq.imtls.array)
if oq.hazard_calculation_id is None:
self.rupser.close()
num_events = sum(set_counts(self.datastore, 'events').values())
if num_events == 0:
raise RuntimeError(
'No seismic events! Perhaps the investigation time is too '
'small or the maximum_distance is too small')
if oq.save_ruptures:
logging.info('Setting %d event years on %d ruptures',
num_events, self.rupser.nruptures)
with self.monitor('setting event years',
measuremem=True,
autoflush=True):
numpy.random.seed(self.oqparam.ses_seed)
set_random_years(self.datastore, 'events',
int(self.oqparam.investigation_time))
if self.gmf_size:
self.datastore.set_attrs('events', max_gmf_size=self.gmf_size)
msg = 'less than ' if self.get_min_iml(self.oqparam).sum() else ''
logging.info('Generating %s%s of GMFs', msg,
humansize(self.gmf_size))
if oq.hazard_curves_from_gmfs:
rlzs = self.csm_info.rlzs_assoc.realizations
# compute and save statistics; this is done in process and can
# be very slow if there are thousands of realizations
weights = [rlz.weight for rlz in rlzs]
hstats = self.oqparam.hazard_stats()
if len(hstats):
logging.info('Computing statistical hazard curves')
for kind, stat in hstats:
pmap = compute_pmap_stats(result.values(), [stat], weights)
arr = numpy.zeros((N, L), F32)
for sid in pmap:
arr[sid] = pmap[sid].array[:, 0]
self.datastore['hcurves/' + kind] = arr
self.save_hmaps()
if self.datastore.parent:
self.datastore.parent.open('r')
if 'gmf_data' in self.datastore:
self.save_gmf_bytes()
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# one could also set oq.number_of_logic_tree_samples = 0
self.cl = ClassicalCalculator(oq)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(close=False)
cl_mean_curves = get_mean_curves(self.cl.datastore)
eb_mean_curves = get_mean_curves(self.datastore)
rdiff, index = util.max_rel_diff_index(cl_mean_curves,
eb_mean_curves)
logging.warn(
'Relative difference with the classical '
'mean curves: %d%% at site index %d', rdiff * 100, index)
def post_execute(self, result):
oq = self.oqparam
if not oq.ground_motion_fields and not oq.hazard_curves_from_gmfs:
return
N = len(self.sitecol.complete)
M = len(oq.imtls)
L = len(oq.imtls.array)
L1 = L // M
if result and oq.hazard_curves_from_gmfs:
rlzs = self.datastore['full_lt'].get_realizations()
# compute and save statistics; this is done in process and can
# be very slow if there are thousands of realizations
weights = [rlz.weight for rlz in rlzs]
# NB: in the future we may want to save to individual hazard
# curves if oq.individual_curves is set; for the moment we
# save the statistical curves only
hstats = oq.hazard_stats()
S = len(hstats)
pmaps = list(result.values())
R = len(weights)
if len(pmaps) != R:
# this should never happen, unless I break the
# logic tree reduction mechanism during refactoring
raise AssertionError('Expected %d pmaps, got %d' %
(len(weights), len(pmaps)))
if oq.individual_curves:
logging.info('Saving individual hazard curves')
self.datastore.create_dset('hcurves-rlzs', F32, (N, R, M, L1))
self.datastore.set_shape_attrs('hcurves-rlzs',
site_id=N,
rlz_id=R,
imt=list(oq.imtls),
lvl=numpy.arange(L1))
if oq.poes:
P = len(oq.poes)
M = len(oq.imtls)
ds = self.datastore.create_dset('hmaps-rlzs', F32,
(N, R, M, P))
self.datastore.set_shape_attrs('hmaps-rlzs',
site_id=N,
rlz_id=R,
imt=list(oq.imtls),
poe=oq.poes)
for r, pmap in enumerate(pmaps):
arr = numpy.zeros((N, M, L1), F32)
for sid in pmap:
arr[sid] = pmap[sid].array.reshape(M, L1)
self.datastore['hcurves-rlzs'][:, r] = arr
if oq.poes:
hmap = calc.make_hmap(pmap, oq.imtls, oq.poes)
for sid in hmap:
ds[sid, r] = hmap[sid].array
if S:
logging.info('Computing statistical hazard curves')
self.datastore.create_dset('hcurves-stats', F32, (N, S, M, L1))
self.datastore.set_shape_attrs('hcurves-stats',
site_id=N,
stat=list(hstats),
imt=list(oq.imtls),
lvl=numpy.arange(L1))
if oq.poes:
P = len(oq.poes)
M = len(oq.imtls)
ds = self.datastore.create_dset('hmaps-stats', F32,
(N, S, M, P))
self.datastore.set_shape_attrs('hmaps-stats',
site_id=N,
stat=list(hstats),
imt=list(oq.imtls),
poes=oq.poes)
for s, stat in enumerate(hstats):
pmap = compute_pmap_stats(pmaps, [hstats[stat]], weights,
oq.imtls)
arr = numpy.zeros((N, M, L1), F32)
for sid in pmap:
arr[sid] = pmap[sid].array.reshape(M, L1)
self.datastore['hcurves-stats'][:, s] = arr
if oq.poes:
hmap = calc.make_hmap(pmap, oq.imtls, oq.poes)
for sid in hmap:
ds[sid, s] = hmap[sid].array
if self.datastore.parent:
self.datastore.parent.open('r')
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
job_id = logs.init('job')
oq.calculation_mode = 'classical'
self.cl = ClassicalCalculator(oq, job_id)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run()
engine.expose_outputs(self.datastore)
for imt in oq.imtls:
cl_mean_curves = get_mean_curves(self.datastore, imt)
eb_mean_curves = get_mean_curves(self.datastore, imt)
self.rdiff, index = util.max_rel_diff_index(
cl_mean_curves, eb_mean_curves)
logging.warning(
'Relative difference with the classical '
'mean curves: %d%% at site index %d, imt=%s',
self.rdiff * 100, index, imt)
def post_execute(self, result):
"""
Save the SES collection
"""
oq = self.oqparam
if 'ucerf' in oq.calculation_mode:
self.rupser.close()
self.csm.info.update_eff_ruptures(self.csm.get_num_ruptures())
self.setting_events()
N = len(self.sitecol.complete)
L = len(oq.imtls.array)
if result and oq.hazard_curves_from_gmfs:
rlzs = self.csm_info.get_rlzs_assoc().realizations
# compute and save statistics; this is done in process and can
# be very slow if there are thousands of realizations
weights = [rlz.weight for rlz in rlzs]
# NB: in the future we may want to save to individual hazard
# curves if oq.individual_curves is set; for the moment we
# save the statistical curves only
hstats = oq.hazard_stats()
if len(hstats):
logging.info('Computing statistical hazard curves')
for statname, stat in hstats:
pmap = compute_pmap_stats(result.values(), [stat], weights)
arr = numpy.zeros((N, L), F32)
for sid in pmap:
arr[sid] = pmap[sid].array[:, 0]
self.datastore['hcurves/' + statname] = arr
if oq.poes:
P = len(oq.poes)
I = len(oq.imtls)
self.datastore.create_dset(
'hmaps/' + statname, F32, (N, P * I))
self.datastore.set_attrs(
'hmaps/' + statname, nbytes=N * P * I * 4)
hmap = calc.make_hmap(pmap, oq.imtls, oq.poes)
ds = self.datastore['hmaps/' + statname]
for sid in hmap:
ds[sid] = hmap[sid].array[:, 0]
if self.datastore.parent:
self.datastore.parent.open('r')
if 'gmf_data' in self.datastore:
self.save_gmf_bytes()
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
# one could also set oq.number_of_logic_tree_samples = 0
self.cl = ClassicalCalculator(oq)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(close=False)
cl_mean_curves = get_mean_curves(self.cl.datastore)
eb_mean_curves = get_mean_curves(self.datastore)
rdiff, index = util.max_rel_diff_index(
cl_mean_curves, eb_mean_curves)
logging.warn('Relative difference with the classical '
'mean curves: %d%% at site index %d',
rdiff * 100, index)
def post_execute(self, result):
oq = self.oqparam
if not oq.ground_motion_fields:
return
N = len(self.sitecol.complete)
L = len(oq.imtls.array)
if result and oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
# compute and save statistics; this is done in process and can
# be very slow if there are thousands of realizations
weights = [rlz.weight for rlz in rlzs]
# NB: in the future we may want to save to individual hazard
# curves if oq.individual_curves is set; for the moment we
# save the statistical curves only
hstats = oq.hazard_stats()
pmaps = list(result.values())
if len(hstats):
logging.info('Computing statistical hazard curves')
if len(weights) != len(pmaps):
# this should never happen, unless I break the
# logic tree reduction mechanism during refactoring
raise AssertionError('Expected %d pmaps, got %d' %
(len(weights), len(pmaps)))
for statname, stat in hstats:
pmap = compute_pmap_stats(pmaps, [stat], weights, oq.imtls)
arr = numpy.zeros((N, L), F32)
for sid in pmap:
arr[sid] = pmap[sid].array[:, 0]
self.datastore['hcurves/' + statname] = arr
if oq.poes:
P = len(oq.poes)
M = len(oq.imtls)
self.datastore.create_dset(
'hmaps/' + statname, F32, (N, M, P))
self.datastore.set_attrs(
'hmaps/' + statname, nbytes=N * P * M * 4)
hmap = calc.make_hmap(pmap, oq.imtls, oq.poes)
ds = self.datastore['hmaps/' + statname]
for sid in hmap:
ds[sid] = hmap[sid].array
if self.datastore.parent:
self.datastore.parent.open('r')
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
job_id = logs.init('job')
self.cl = ClassicalCalculator(oq, job_id)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(close=False)
cl_mean_curves = get_mean_curves(self.cl.datastore)
eb_mean_curves = get_mean_curves(self.datastore)
rdiff, index = util.max_rel_diff_index(
cl_mean_curves, eb_mean_curves)
logging.warning('Relative difference with the classical '
'mean curves: %d%% at site index %d',
rdiff * 100, index)
def post_execute(self, result):
oq = self.oqparam
if not oq.ground_motion_fields:
return
N = len(self.sitecol.complete)
L = len(oq.imtls.array)
if result and oq.hazard_curves_from_gmfs:
rlzs = self.rlzs_assoc.realizations
# compute and save statistics; this is done in process and can
# be very slow if there are thousands of realizations
weights = [rlz.weight for rlz in rlzs]
# NB: in the future we may want to save to individual hazard
# curves if oq.individual_curves is set; for the moment we
# save the statistical curves only
hstats = oq.hazard_stats()
S = len(hstats)
pmaps = list(result.values())
R = len(weights)
if len(pmaps) != R:
# this should never happen, unless I break the
# logic tree reduction mechanism during refactoring
raise AssertionError('Expected %d pmaps, got %d' %
(len(weights), len(pmaps)))
if oq.individual_curves:
logging.info('Saving individual hazard curves')
self.datastore.create_dset('hcurves-rlzs', F32, (N, R, L))
self.datastore.set_attrs('hcurves-rlzs', nbytes=N * R * L * 4)
if oq.poes:
P = len(oq.poes)
M = len(oq.imtls)
ds = self.datastore.create_dset(
'hmaps-rlzs', F32, (N, R, M, P))
self.datastore.set_attrs(
'hmaps-rlzs', nbytes=N * R * P * M * 4)
for r, pmap in enumerate(pmaps):
arr = numpy.zeros((N, L), F32)
for sid in pmap:
arr[sid] = pmap[sid].array[:, 0]
self.datastore['hcurves-rlzs'][:, r] = arr
if oq.poes:
hmap = calc.make_hmap(pmap, oq.imtls, oq.poes)
for sid in hmap:
ds[sid, r] = hmap[sid].array
if S:
logging.info('Computing statistical hazard curves')
self.datastore.create_dset('hcurves-stats', F32, (N, S, L))
self.datastore.set_attrs('hcurves-stats', nbytes=N * S * L * 4)
if oq.poes:
P = len(oq.poes)
M = len(oq.imtls)
ds = self.datastore.create_dset(
'hmaps-stats', F32, (N, S, M, P))
self.datastore.set_attrs(
'hmaps-stats', nbytes=N * S * P * M * 4)
for s, stat in enumerate(hstats):
pmap = compute_pmap_stats(
pmaps, [hstats[stat]], weights, oq.imtls)
arr = numpy.zeros((N, L), F32)
for sid in pmap:
arr[sid] = pmap[sid].array[:, 0]
self.datastore['hcurves-stats'][:, s] = arr
if oq.poes:
hmap = calc.make_hmap(pmap, oq.imtls, oq.poes)
for sid in hmap:
ds[sid, s] = hmap[sid].array
if self.datastore.parent:
self.datastore.parent.open('r')
if oq.compare_with_classical: # compute classical curves
export_dir = os.path.join(oq.export_dir, 'cl')
if not os.path.exists(export_dir):
os.makedirs(export_dir)
oq.export_dir = export_dir
job_id = logs.init('job')
self.cl = ClassicalCalculator(oq, job_id)
# TODO: perhaps it is possible to avoid reprocessing the source
# model, however usually this is quite fast and do not dominate
# the computation
self.cl.run(close=False)
engine.expose_outputs(self.cl.datastore)
cl_mean_curves = get_mean_curves(self.cl.datastore)
eb_mean_curves = get_mean_curves(self.datastore)
self.rdiff, index = util.max_rel_diff_index(
cl_mean_curves, eb_mean_curves)
logging.warning('Relative difference with the classical '
'mean curves: %d%% at site index %d',
self.rdiff * 100, index)