def compute_losses(ssm, src_filter, param, riskmodel, monitor):
"""
Compute the losses for a single source model. Returns the ruptures
as an attribute `.ruptures_by_grp` of the list of losses.
:param ssm: CompositeSourceModel containing a single source model
:param sitecol: a SiteCollection instance
:param param: a dictionary of extra parameters
:param riskmodel: a RiskModel instance
:param monitor: a Monitor instance
:returns: a List containing the losses by taxonomy and some attributes
"""
[grp] = ssm.src_groups
res = List()
rlzs_assoc = ssm.info.get_rlzs_assoc()
rlzs_by_gsim = rlzs_assoc.get_rlzs_by_gsim(DEFAULT_TRT)
hazard = compute_hazard(grp, src_filter, rlzs_by_gsim, param, monitor)
[(grp_id, ebruptures)] = hazard['ruptures'].items()
samples = ssm.info.get_samples_by_grp()
num_rlzs = len(rlzs_assoc.realizations)
rlzs_by_gsim = rlzs_assoc.get_rlzs_by_gsim(DEFAULT_TRT)
getter = getters.GmfGetter(rlzs_by_gsim, ebruptures, src_filter.sitecol,
param['oqparam'], param['min_iml'],
samples[grp_id])
ri = riskinput.RiskInput(getter, param['assetcol'].assets_by_site())
res.append(ucerf_risk(ri, riskmodel, param, monitor))
res.sm_id = ssm.sm_id
res.num_events = len(ri.hazard_getter.eids)
start = res.sm_id * num_rlzs
res.rlz_slice = slice(start, start + num_rlzs)
res.events_by_grp = hazard.events_by_grp
res.eff_ruptures = hazard.eff_ruptures
return res
def ebrisk(rupgetters, srcfilter, param, monitor):
"""
:param rupgetters: RuptureGetters with 1 rupture each
:param srcfilter: a SourceFilter
:param param: dictionary of parameters coming from oqparam
:param monitor: a Monitor instance
:returns: a dictionary with keys elt, alt, ...
"""
mon_haz = monitor('getting hazard', measuremem=False)
computers = []
with monitor('getting ruptures'):
for rupgetter in rupgetters:
gg = getters.GmfGetter(rupgetter, srcfilter, param['oqparam'])
gg.init()
computers.extend(gg.computers)
if not computers: # all filtered out
return {}
rupgetters.clear()
computers.sort(key=lambda c: c.rupture.ridx)
hazard = dict(gmfs=[], events=[], gmf_info=[])
for c in computers:
with mon_haz:
data = c.compute_all(gg.min_iml, gg.rlzs_by_gsim)
hazard['gmfs'].append(data)
hazard['events'].append(c.rupture.get_events(gg.rlzs_by_gsim))
hazard['gmf_info'].append((c.rupture.ridx, mon_haz.task_no,
len(c.sids), data.nbytes, mon_haz.dt))
computers.clear()
acc = _calc_risk(hazard, param, monitor)
return acc
def ebrisk(rupgetter, param, monitor):
"""
:param rupgetter: RuptureGetter with multiple ruptures
:param param: dictionary of parameters coming from oqparam
:param monitor: a Monitor instance
:returns: a dictionary of arrays
"""
mon_rup = monitor('getting ruptures', measuremem=False)
mon_haz = monitor('getting hazard', measuremem=True)
alldata = general.AccumDict(accum=[])
gmf_info = []
srcfilter = monitor.read('srcfilter')
param['N'] = len(srcfilter.sitecol.complete)
gg = getters.GmfGetter(rupgetter, srcfilter, param['oqparam'],
param['amplifier'])
with mon_haz:
for c in gg.gen_computers(mon_rup):
data, time_by_rup = c.compute_all(gg.min_iml, gg.rlzs_by_gsim)
if len(data):
for key, val in data.items():
alldata[key].extend(data[key])
nbytes = len(data['sid']) * len(data) * 4
gmf_info.append((c.ebrupture.id, mon_haz.task_no, len(c.sids),
nbytes, mon_haz.dt))
if not alldata:
return {}
for key, val in sorted(alldata.items()):
if key in 'eid sid rlz':
alldata[key] = U32(alldata[key])
else:
alldata[key] = F32(alldata[key])
yield from event_based_risk(pandas.DataFrame(alldata), param, monitor)
if gmf_info:
yield {'gmf_info': numpy.array(gmf_info, gmf_info_dt)}
def ebrisk(rupgetter, param, monitor):
"""
:param rupgetter: RuptureGetter with multiple ruptures
:param param: dictionary of parameters coming from oqparam
:param monitor: a Monitor instance
:returns: a dictionary with keys elt, alt, ...
"""
mon_rup = monitor('getting ruptures', measuremem=False)
mon_haz = monitor('getting hazard', measuremem=True)
gmfs = []
gmf_info = []
srcfilter = monitor.read('srcfilter')
gg = getters.GmfGetter(rupgetter, srcfilter, param['oqparam'],
param['amplifier'])
nbytes = 0
with mon_haz:
for c in gg.gen_computers(mon_rup):
data, time_by_rup = c.compute_all(gg.min_iml, gg.rlzs_by_gsim)
if len(data):
gmfs.append(data)
nbytes += data.nbytes
gmf_info.append((c.ebrupture.id, mon_haz.task_no, len(c.sids),
data.nbytes, mon_haz.dt))
if not gmfs:
return {}
res = calc_risk(numpy.concatenate(gmfs), param, monitor)
if gmf_info:
res['gmf_info'] = numpy.array(gmf_info, gmf_info_dt)
return res
def ebrisk(rupgetter, srcfilter, param, monitor):
"""
:param rupgetter: RuptureGetter with multiple ruptures
:param srcfilter: a SourceFilter
:param param: dictionary of parameters coming from oqparam
:param monitor: a Monitor instance
:returns: a dictionary with keys elt, alt, ...
"""
mon_haz = monitor('getting hazard', measuremem=False)
mon_rup = monitor('getting ruptures', measuremem=False)
hazard = dict(gmfs=[], events=[], gmf_info=[])
for rg in rupgetter.split():
with mon_rup:
gg = getters.GmfGetter(rg, srcfilter, param['oqparam'])
gg.init()
if not gg.computers: # filtered out rupture
continue
[c] = gg.computers
with mon_haz:
data = c.compute_all(gg.min_iml, gg.rlzs_by_gsim)
hazard['gmfs'].append(data)
hazard['events'].append(c.rupture.get_events(gg.rlzs_by_gsim))
hazard['gmf_info'].append(
(c.rupture.id, mon_haz.task_no, len(c.sids),
data.nbytes, mon_haz.dt))
if not hazard['gmfs']:
return {}
with monitor('getting assets'):
assetcol = datastore.read(param['hdf5path'])['assetcol']
return calc_risk(hazard, assetcol, param, monitor)
def compute_hazard(sources, src_filter, rlzs_by_gsim, param, monitor):
"""
:param sources: a list with a single UCERF source
:param src_filter: a SourceFilter instance
:param rlzs_by_gsim: a dictionary gsim -> rlzs
:param param: extra parameters
:param monitor: a Monitor instance
:returns: an AccumDict grp_id -> EBRuptures
"""
[src] = sources
res = AccumDict()
res.calc_times = []
serial = 1
sampl_mon = monitor('sampling ruptures', measuremem=True)
filt_mon = monitor('filtering ruptures', measuremem=False)
res.trt = DEFAULT_TRT
ebruptures = []
background_sids = src.get_background_sids(src_filter)
sitecol = src_filter.sitecol
cmaker = ContextMaker(rlzs_by_gsim, src_filter.integration_distance)
for sample in range(param['samples']):
for ses_idx, ses_seed in param['ses_seeds']:
seed = sample * TWO16 + ses_seed
with sampl_mon:
rups, n_occs = generate_event_set(src, background_sids,
src_filter, seed)
with filt_mon:
for rup, n_occ in zip(rups, n_occs):
rup.serial = serial
try:
rup.sctx, rup.dctx = cmaker.make_contexts(sitecol, rup)
indices = rup.sctx.sids
except FarAwayRupture:
continue
events = []
for _ in range(n_occ):
events.append((0, src.src_group_id, ses_idx, sample))
if events:
evs = numpy.array(events, stochastic.event_dt)
ebruptures.append(EBRupture(rup, src.id, indices, evs))
serial += 1
res.num_events = len(stochastic.set_eids(ebruptures))
res['ruptures'] = {src.src_group_id: ebruptures}
if param['save_ruptures']:
res.ruptures_by_grp = {src.src_group_id: ebruptures}
else:
res.events_by_grp = {
src.src_group_id: event_based.get_events(ebruptures)
}
res.eff_ruptures = {src.src_group_id: src.num_ruptures}
if param.get('gmf'):
getter = getters.GmfGetter(rlzs_by_gsim, ebruptures, sitecol,
param['oqparam'], param['min_iml'],
param['samples'])
res.update(getter.compute_gmfs_curves(monitor))
return res
def compute_hazard(sources, src_filter, rlzs_by_gsim, param, monitor):
"""
:param sources: a list with a single UCERF source
:param src_filter: a SourceFilter instance
:param rlzs_by_gsim: a dictionary gsim -> rlzs
:param param: extra parameters
:param monitor: a Monitor instance
:returns: an AccumDict grp_id -> EBRuptures
"""
[src] = sources
res = AccumDict()
res.calc_times = []
serial = 1
sampl_mon = monitor('sampling ruptures', measuremem=True)
filt_mon = monitor('filtering ruptures', measuremem=False)
res.trt = DEFAULT_TRT
background_sids = src.get_background_sids(src_filter)
sitecol = src_filter.sitecol
cmaker = ContextMaker(rlzs_by_gsim, src_filter.integration_distance)
num_ses = param['ses_per_logic_tree_path']
samples = getattr(src, 'samples', 1)
n_occ = AccumDict(accum=numpy.zeros((samples, num_ses), numpy.uint16))
with sampl_mon:
for sam_idx in range(samples):
for ses_idx, ses_seed in param['ses_seeds']:
seed = sam_idx * TWO16 + ses_seed
rups, occs = generate_event_set(src, background_sids,
src_filter, seed)
for rup, occ in zip(rups, occs):
n_occ[rup][sam_idx, ses_idx] = occ
rup.serial = serial
serial += 1
with filt_mon:
rlzs = numpy.concatenate(list(rlzs_by_gsim.values()))
ebruptures = stochastic.build_eb_ruptures(src, rlzs, num_ses, cmaker,
sitecol, n_occ.items())
res.num_events = sum(ebr.multiplicity for ebr in ebruptures)
res['ruptures'] = {src.src_group_id: ebruptures}
if param['save_ruptures']:
res.ruptures_by_grp = {src.src_group_id: ebruptures}
else:
res.events_by_grp = {
src.src_group_id: event_based.get_events(ebruptures)
}
res.eff_ruptures = {src.src_group_id: src.num_ruptures}
if param.get('gmf'):
getter = getters.GmfGetter(rlzs_by_gsim, ebruptures, sitecol,
param['oqparam'], param['min_iml'], samples)
res.update(getter.compute_gmfs_curves(monitor))
return res
def compute_losses(ssm, src_filter, param, riskmodel,
imts, trunc_level, correl_model, min_iml, monitor):
"""
Compute the losses for a single source model. Returns the ruptures
as an attribute `.ruptures_by_grp` of the list of losses.
:param ssm: CompositeSourceModel containing a single source model
:param sitecol: a SiteCollection instance
:param param: a dictionary of parameters
:param riskmodel: a RiskModel instance
:param imts: a list of Intensity Measure Types
:param trunc_level: truncation level
:param correl_model: correlation model
:param min_iml: vector of minimum intensities, one per IMT
:param monitor: a Monitor instance
:returns: a List containing the losses by taxonomy and some attributes
"""
[grp] = ssm.src_groups
res = List()
gsims = ssm.gsim_lt.values[DEFAULT_TRT]
ruptures_by_grp = compute_ruptures(
grp, src_filter, gsims, param, monitor)
[(grp_id, ebruptures)] = ruptures_by_grp.items()
rlzs_assoc = ssm.info.get_rlzs_assoc()
samples = ssm.info.get_samples_by_grp()
num_rlzs = len(rlzs_assoc.realizations)
rlzs_by_gsim = rlzs_assoc.get_rlzs_by_gsim(DEFAULT_TRT)
getter = getters.GmfGetter(
rlzs_by_gsim, ebruptures, src_filter.sitecol, imts, min_iml,
src_filter.integration_distance, trunc_level, correl_model,
samples[grp_id])
ri = riskinput.RiskInput(getter, param['assetcol'].assets_by_site())
res.append(event_based_risk(ri, riskmodel, param, monitor))
res.sm_id = ssm.sm_id
res.num_events = len(ri.hazard_getter.eids)
start = res.sm_id * num_rlzs
res.rlz_slice = slice(start, start + num_rlzs)
res.events_by_grp = ruptures_by_grp.events_by_grp
res.eff_ruptures = ruptures_by_grp.eff_ruptures
return res
def ebrisk(rupgetter, srcfilter, param, monitor):
"""
:param rupgetter: RuptureGetter with multiple ruptures
:param srcfilter: a SourceFilter
:param param: dictionary of parameters coming from oqparam
:param monitor: a Monitor instance
:returns: a dictionary with keys elt, alt, ...
"""
mon_rup = monitor('getting ruptures', measuremem=False)
mon_haz = monitor('getting hazard', measuremem=False)
gmfs = []
gmf_info = []
gg = getters.GmfGetter(rupgetter, srcfilter, param['oqparam'],
param['amplifier'])
nbytes = 0
for c in gg.gen_computers(mon_rup):
with mon_haz:
data, time_by_rup = c.compute_all(gg.min_iml, gg.rlzs_by_gsim)
if len(data):
gmfs.append(data)
nbytes += data.nbytes
gmf_info.append((c.ebrupture.id, mon_haz.task_no, len(c.sids),
data.nbytes, mon_haz.dt))
if nbytes > param['ebrisk_maxsize']:
msg = 'produced subtask'
try:
logs.dbcmd('log', monitor.calc_id, datetime.utcnow(), 'DEBUG',
'ebrisk#%d' % monitor.task_no, msg)
except Exception: # for `oq run`
print(msg)
yield calc_risk, numpy.concatenate(gmfs), param
nbytes = 0
gmfs = []
res = {}
if gmfs:
res.update(calc_risk(numpy.concatenate(gmfs), param, monitor))
if gmf_info:
res['gmf_info'] = numpy.array(gmf_info, gmf_info_dt)
yield res
def ebrisk(rupgetter, srcfilter, param, monitor):
"""
:param rupgetter:
a RuptureGetter instance
:param srcfilter:
a SourceFilter instance
:param param:
a dictionary of parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:returns:
an ArrayWrapper with shape (E, L, T, ...)
"""
riskmodel = param['riskmodel']
E = rupgetter.num_events
L = len(riskmodel.lti)
N = len(srcfilter.sitecol.complete)
e1 = rupgetter.first_event
with monitor('getting assets', measuremem=False):
with datastore.read(srcfilter.filename) as dstore:
assetcol = dstore['assetcol']
assets_by_site = assetcol.assets_by_site()
A = len(assetcol)
getter = getters.GmfGetter(rupgetter, srcfilter, param['oqparam'])
with monitor('getting hazard'):
getter.init() # instantiate the computers
hazard = getter.get_hazard_by_sid() # sid -> (sid, eid, gmv)
mon_risk = monitor('computing risk', measuremem=False)
mon_agg = monitor('aggregating losses', measuremem=False)
events = rupgetter.get_eid_rlz()
# numpy.testing.assert_equal(events['eid'], sorted(events['eid']))
eid2idx = dict(zip(events['eid'], range(e1, e1 + E)))
tagnames = param['aggregate_by']
shape = assetcol.tagcol.agg_shape((E, L), tagnames)
elt_dt = [('eid', U64), ('rlzi', U16), ('loss', (F32, shape[1:]))]
if param['asset_loss_table']:
alt = numpy.zeros((A, E, L), F32)
acc = numpy.zeros(shape, F32) # shape (E, L, T...)
if param['avg_losses']:
losses_by_A = numpy.zeros((A, L), F32)
else:
losses_by_A = 0
# NB: IMT-dependent weights are not supported in ebrisk
times = numpy.zeros(N) # risk time per site_id
num_events_per_sid = 0
epspath = param['epspath']
gmf_nbytes = 0
for sid, haz in hazard.items():
gmf_nbytes += haz.nbytes
t0 = time.time()
assets_on_sid = assets_by_site[sid]
if len(assets_on_sid) == 0:
continue
num_events_per_sid += len(haz)
if param['avg_losses']:
weights = getter.weights[[
getter.eid2rlz[eid] for eid in haz['eid']
]]
assets_by_taxo = get_assets_by_taxo(assets_on_sid, epspath)
eidx = numpy.array([eid2idx[eid] for eid in haz['eid']]) - e1
haz['eid'] = eidx + e1
with mon_risk:
out = riskmodel.get_output(assets_by_taxo, haz)
with mon_agg:
for a, asset in enumerate(assets_on_sid):
aid = asset['ordinal']
tagi = asset[tagnames] if tagnames else ()
tagidxs = tuple(idx - 1 for idx in tagi)
for lti, lt in enumerate(riskmodel.loss_types):
lratios = out[lt][a]
if lt == 'occupants':
losses = lratios * asset['occupants_None']
else:
losses = lratios * asset['value-' + lt]
if param['asset_loss_table']:
alt[aid, eidx, lti] = losses
acc[(eidx, lti) + tagidxs] += losses
if param['avg_losses']:
losses_by_A[aid, lti] += losses @ weights
times[sid] = time.time() - t0
if hazard:
num_events_per_sid /= len(hazard)
with monitor('building event loss table'):
elt = numpy.fromiter(
((event['eid'], event['rlz'], losses)
for event, losses in zip(events, acc) if losses.sum()), elt_dt)
agg = general.AccumDict(accum=numpy.zeros(shape[1:], F32)) # rlz->agg
for rec in elt:
agg[rec['rlzi']] += rec['loss'] * param['ses_ratio']
res = {
'elt': elt,
'agg_losses': agg,
'times': times,
'events_per_sid': num_events_per_sid,
'gmf_nbytes': gmf_nbytes
}
if param['avg_losses']:
res['losses_by_A'] = losses_by_A * param['ses_ratio']
if param['asset_loss_table']:
eidx = numpy.array([eid2idx[eid] for eid in events['eid']])
res['alt_eidx'] = alt, eidx
return res
def ebrisk(rupgetter, srcfilter, param, monitor):
"""
:param rupgetter:
a RuptureGetter instance
:param srcfilter:
a SourceFilter instance
:param param:
a dictionary of parameters
:param monitor:
:class:`openquake.baselib.performance.Monitor` instance
:returns:
an ArrayWrapper with shape (E, L, T, ...)
"""
riskmodel = param['riskmodel']
L = len(riskmodel.lti)
N = len(srcfilter.sitecol.complete)
mon = monitor('getting assets', measuremem=False)
with datastore.read(srcfilter.filename) as dstore:
assgetter = getters.AssetGetter(dstore)
getter = getters.GmfGetter(rupgetter, srcfilter, param['oqparam'])
with monitor('getting hazard'):
getter.init() # instantiate the computers
hazard = getter.get_hazard() # sid -> (rlzi, sid, eid, gmv)
mon_risk = monitor('computing losses', measuremem=False)
mon_agg = monitor('aggregating losses', measuremem=False)
imts = getter.imts
events = rupgetter.get_eid_rlz()
eid2idx = {eid: idx for idx, eid in enumerate(events['eid'])}
tagnames = param['aggregate_by']
shape = assgetter.tagcol.agg_shape((len(events), L), tagnames)
elt_dt = [('eid', U64), ('rlzi', U16), ('loss', (F32, shape[1:]))]
acc = numpy.zeros(shape, F32) # shape (E, L, T...)
if param['avg_losses']:
losses_by_A = numpy.zeros((assgetter.num_assets, L), F32)
else:
losses_by_A = 0
times = numpy.zeros(N) # risk time per site_id
for sid, haz in hazard.items():
t0 = time.time()
weights = getter.weights[haz['rlzi']]
assets_on_sid, tagidxs = assgetter.get(sid, tagnames)
eidx = [eid2idx[eid] for eid in haz['eid']]
mon.duration += time.time() - t0
mon.counts += 1
with mon_risk:
assets_ratios = riskmodel.get_assets_ratios(
assets_on_sid, haz['gmv'], imts)
with mon_agg:
for assets, ratios in assets_ratios:
taxo = assets[0].taxonomy
ws_by_lti = [
weights[vf.imt]
for vf in riskmodel[taxo].risk_functions.values()
]
for lti, loss_ratios in enumerate(ratios):
ws = ws_by_lti[lti]
lt = riskmodel.loss_types[lti]
for asset in assets:
aid = asset.ordinal
losses = loss_ratios * asset.value(lt)
acc[(eidx, lti) + tagidxs[aid]] += losses
if param['avg_losses']:
losses_by_A[aid, lti] += losses @ ws
times[sid] = time.time() - t0
with monitor('building event loss table'):
elt = numpy.fromiter(
((event['eid'], event['rlz'], losses)
for event, losses in zip(events, acc) if losses.sum()), elt_dt)
agg = general.AccumDict(accum=numpy.zeros(shape[1:], F32)) # rlz->agg
for rec in elt:
agg[rec['rlzi']] += rec['loss'] * param['ses_ratio']
res = {'elt': elt, 'agg_losses': agg, 'times': times}
if param['avg_losses']:
res['losses_by_A'] = losses_by_A * param['ses_ratio']
return res
def start_tasks(self, sm_id, sitecol, assetcol, riskmodel, imtls,
trunc_level, correl_model, min_iml):
"""
:param sm_id: source model ordinal
:param sitecol: a SiteCollection instance
:param assetcol: an AssetCollection instance
:param riskmodel: a RiskModel instance
:param imtls: Intensity Measure Types and Levels
:param trunc_level: truncation level
:param correl_model: correlation model
:param min_iml: vector of minimum intensities, one per IMT
:returns: an IterResult instance
"""
sm_info = self.csm_info.get_info(sm_id)
grp_ids = sorted(sm_info.get_sm_by_grp())
rlzs_assoc = sm_info.get_rlzs_assoc()
# prepare the risk inputs
allargs = []
ruptures_per_block = self.oqparam.ruptures_per_block
try:
csm_info = self.csm.info
except AttributeError: # there is no .csm if --hc was given
csm_info = self.datastore['csm_info']
samples_by_grp = csm_info.get_samples_by_grp()
num_events = 0
num_ruptures = {}
taskname = '%s#%d' % (event_based_risk.__name__, sm_id + 1)
monitor = self.monitor(taskname)
for grp_id in grp_ids:
ruptures = self.ruptures_by_grp.get(grp_id, [])
rlzs_by_gsim = rlzs_assoc.get_rlzs_by_gsim(grp_id)
samples = samples_by_grp[grp_id]
num_ruptures[grp_id] = len(ruptures)
from_parent = hasattr(ruptures, 'split')
if from_parent: # read the ruptures from the parent datastore
logging.info('Reading ruptures group #%d', grp_id)
with self.monitor('reading ruptures', measuremem=True):
blocks = ruptures.split(ruptures_per_block)
else: # the ruptures are already in memory
blocks = block_splitter(ruptures, ruptures_per_block)
for rupts in blocks:
n_events = (rupts.n_events if from_parent
else sum(ebr.multiplicity for ebr in rupts))
eps = self.get_eps(self.start, self.start + n_events)
num_events += n_events
self.start += n_events
getter = getters.GmfGetter(
rlzs_by_gsim, rupts, sitecol, imtls, min_iml,
self.oqparam.maximum_distance, trunc_level, correl_model,
self.oqparam.filter_distance, samples)
ri = riskinput.RiskInput(getter, self.assets_by_site, eps)
allargs.append((ri, riskmodel, assetcol, monitor))
if self.datastore.parent: # avoid hdf5 fork issues
self.datastore.parent.close()
ires = parallel.Starmap(
event_based_risk, allargs, name=taskname).submit_all()
ires.num_ruptures = num_ruptures
ires.num_events = num_events
ires.num_rlzs = len(rlzs_assoc.realizations)
ires.sm_id = sm_id
return ires
def eval_calc(self):
"""
Evaluate each calculators for different IMs
"""
# Define the GmfGetter
#for args_tag in range(len(self.args)-1):
# Looping over all source models (Note: the last attribute in self.args is a monitor - so skipping it)
cur_getter = getters.GmfGetter(
self.args[0][0],
calc.filters.SourceFilter(self.dstore['sitecol'],
self.dstore['oqparam'].maximum_distance),
self.calculator.param['oqparam'],
self.calculator.param['amplifier'],
self.calculator.param['sec_perils'])
print('eval_calc: cur_getter = ')
print(cur_getter)
# Evaluate each computer
print('FetchOpenQuake: Evaluting ground motion models.')
for computer in cur_getter.gen_computers(self.mon):
# Looping over rupture(s) in the current realization
sids = computer.sids
print('eval_calc: site ID sids = ')
print(sids)
eids_by_rlz = computer.ebrupture.get_eids_by_rlz(
cur_getter.rlzs_by_gsim)
mag = computer.ebrupture.rupture.mag
data = general.AccumDict(accum=[])
cur_T = self.event_info['IntensityMeasure'].get('Periods', None)
for cur_gs, rlzs in cur_getter.rlzs_by_gsim.items():
# Looping over GMPE(s)
print('eval_calc: cur_gs = ')
print(cur_gs)
num_events = sum(len(eids_by_rlz[rlz]) for rlz in rlzs)
if num_events == 0: # it may happen
continue
# NB: the trick for performance is to keep the call to
# .compute outside of the loop over the realizations;
# it is better to have few calls producing big arrays
tmpMean = []
tmpstdtot = []
tmpstdinter = []
tmpstdintra = []
for imti, imt in enumerate(computer.imts):
# Looping over IM(s)
#print('eval_calc: imt = ')
#print(imt)
if str(imt) in ['PGA', 'PGV', 'PGD']:
cur_T = [0.0]
imTag = 'ln' + str(imt)
else:
imTag = 'lnSA'
if isinstance(cur_gs, gsim.multi.MultiGMPE):
gs = cur_gs[str(imt)] # MultiGMPE
else:
gs = cur_gs # regular GMPE
try:
dctx = computer.dctx.roundup(cur_gs.minimum_distance)
if computer.distribution is None:
if computer.correlation_model:
raise ValueError('truncation_level=0 requires '
'no correlation model')
mean, _stddevs = cur_gs.get_mean_and_stddevs(
computer.sctx,
computer.rctx,
dctx,
imt,
stddev_types=[])
num_sids = len(computer.sids)
if cur_gs.DEFINED_FOR_STANDARD_DEVIATION_TYPES == {
const.StdDev.TOTAL
}:
# If the GSIM provides only total standard deviation, we need
# to compute mean and total standard deviation at the sites
# of interest.
# In this case, we also assume no correlation model is used.
if computer.correlation_model:
raise CorrelationButNoInterIntraStdDevs(
computer.correlation_model, cur_gs)
mean, [stddev_total] = cur_gs.get_mean_and_stddevs(
computer.sctx, computer.rctx, dctx, imt,
[const.StdDev.TOTAL])
stddev_total = stddev_total.reshape(
stddev_total.shape + (1, ))
mean = mean.reshape(mean.shape + (1, ))
if imti == 0:
tmpMean = mean
tmpstdtot = stddev_total
else:
tmpMean = np.concatenate((tmpMean, mean),
axis=0)
tmpstdtot = np.concatenate(
(tmpstdtot, stddev_total), axis=0)
else:
mean, [stddev_inter,
stddev_intra] = cur_gs.get_mean_and_stddevs(
computer.sctx, computer.rctx, dctx, imt,
[
const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT
])
stddev_intra = stddev_intra.reshape(
stddev_intra.shape + (1, ))
stddev_inter = stddev_inter.reshape(
stddev_inter.shape + (1, ))
mean = mean.reshape(mean.shape + (1, ))
if imti == 0:
tmpMean = mean
tmpstdinter = stddev_inter
tmpstdintra = stddev_intra
tmpstdtot = np.sqrt(
stddev_inter * stddev_inter +
stddev_intra * stddev_intra)
else:
tmpMean = np.concatenate((tmpMean, mean),
axis=1)
tmpstdinter = np.concatenate(
(tmpstdinter, stddev_inter), axis=1)
tmpstdintra = np.concatenate(
(tmpstdintra, stddev_intra), axis=1)
tmpstdtot = np.concatenate(
(tmpstdtot,
np.sqrt(stddev_inter * stddev_inter +
stddev_intra * stddev_intra)),
axis=1)
except Exception as exc:
raise RuntimeError(
'(%s, %s, source_id=%r) %s: %s' %
(gs, imt, computer.source_id.decode('utf8'),
exc.__class__.__name__, exc)).with_traceback(
exc.__traceback__)
# initialize
gm_collector = []
# transpose
"""
if len(tmpMean):
tmpMean = tmpMean.transpose()
if len(tmpstdinter):
tmpstdinter = tmpstdinter.transpose()
if len(tmpstdintra):
tmpstdintra = tmpstdintra.transpose()
if len(tmpstdtot):
tmpstdtot = tmpstdtot.transpose()
"""
# collect data
for k in range(tmpMean.shape[0]):
imResult = {}
if len(tmpMean):
imResult.update(
{'Mean': [float(x) for x in tmpMean[k].tolist()]})
if len(tmpstdtot):
imResult.update({
'TotalStdDev':
[float(x) for x in tmpstdtot[k].tolist()]
})
if len(tmpstdinter):
imResult.update({
'InterEvStdDev':
[float(x) for x in tmpstdinter[k].tolist()]
})
if len(tmpstdintra):
imResult.update({
'IntraEvStdDev':
[float(x) for x in tmpstdintra[k].tolist()]
})
gm_collector.append({imTag: imResult})
print(gm_collector)
# close datastore instance
self.calculator.datastore.close()
# stop dbserver
cdbs.main('stop')
# Final results
res = {
'Magnitude': mag,
'Periods': cur_T,
'GroundMotions': gm_collector
}
# return
return res