def test_wrong_riskmodel_association(self):
cfm = os.path.join(FF_DIR, 'contents_fragility_model.xml')
# passing a fragility model instead of a consequence model
oq = mock.Mock()
oq.inputs = dict(contents_consequence=cfm)
with self.assertRaises(ValueError) as ctx:
riskmodels.get_risk_models(oq, 'consequence')
self.assertIn('is of kind FragilityModel, '
'expected ConsequenceModel', str(ctx.exception))
def test_wrong_loss_type_association(self):
scm = os.path.join(FF_DIR, 'structural_consequence_model.xml')
ccm = os.path.join(FF_DIR, 'contents_consequence_model.xml')
# exchanging the associations on purpose
oq = mock.Mock()
oq.inputs = dict(structural_consequence=ccm, contents_consequence=scm)
with self.assertRaises(ValueError) as ctx:
riskmodels.get_risk_models(oq, 'consequence')
self.assertIn('structural_consequence_model.xml": lossCategory is of '
'type "structural", expected "contents"',
str(ctx.exception))
def get_pmap_from_csv(oqparam, fnames):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fnames:
a space-separated list of .csv relative filenames
:returns:
the site mesh and the hazard curves read by the .csv files
"""
read = functools.partial(hdf5.read_csv, dtypedict={None: float})
imtls = {}
dic = {}
for wrapper in map(read, fnames):
dic[wrapper.imt] = wrapper.array
imtls[wrapper.imt] = levels_from(wrapper.dtype.names)
oqparam.hazard_imtls = imtls
oqparam.set_risk_imtls(get_risk_models(oqparam))
array = wrapper.array
mesh = geo.Mesh(array['lon'], array['lat'])
num_levels = sum(len(imls) for imls in oqparam.imtls.values())
data = numpy.zeros((len(mesh), num_levels))
level = 0
for im in oqparam.imtls:
arr = dic[im]
for poe in arr.dtype.names[3:]:
data[:, level] = arr[poe]
level += 1
for field in ('lon', 'lat', 'depth'): # sanity check
numpy.testing.assert_equal(arr[field], array[field])
return mesh, ProbabilityMap.from_array(data, range(len(mesh)))
def get_risk_model(oqparam):
"""
Return a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
riskdict = get_risk_models(oqparam)
oqparam.set_risk_imtls(riskdict)
return riskinput.CompositeRiskModel(oqparam, riskdict)
def get_pmap_from_csv(oqparam, fname):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fname:
a .txt file with format `IMT lon lat poe1 ... poeN`
:returns:
the site mesh and the hazard curves read by the .txt file
"""
if not oqparam.imtls:
oqparam.set_risk_imtls(get_risk_models(oqparam))
if not oqparam.imtls:
raise ValueError('Missing intensity_measure_types_and_levels in %s'
% oqparam.inputs['job_ini'])
num_values = list(map(len, list(oqparam.imtls.values())))
with open(oqparam.inputs['hazard_curves']) as csvfile:
mesh, hcurves = get_mesh_csvdata(
csvfile, list(oqparam.imtls), num_values,
valid.decreasing_probabilities)
array = numpy.zeros((len(mesh), sum(num_values)))
for imt_ in hcurves:
array[:, oqparam.imtls.slicedic[imt_]] = hcurves[imt_]
return mesh, ProbabilityMap.from_array(array, range(len(mesh)))
def _read_risk_data(self):
# read the exposure (if any), the risk model (if any) and then the
# site collection, possibly extracted from the exposure.
oq = self.oqparam
self.load_riskmodel() # must be called first
with self.monitor('reading site collection', autoflush=True):
if oq.hazard_calculation_id:
with datastore.read(oq.hazard_calculation_id) as dstore:
haz_sitecol = dstore['sitecol'].complete
else:
haz_sitecol = readinput.get_site_collection(oq)
if hasattr(self, 'rup'):
# for scenario we reduce the site collection to the sites
# within the maximum distance from the rupture
haz_sitecol, _dctx = self.cmaker.filter(
haz_sitecol, self.rup)
haz_sitecol.make_complete()
oq_hazard = (self.datastore.parent['oqparam']
if self.datastore.parent else None)
if 'exposure' in oq.inputs:
self.read_exposure(haz_sitecol)
self.datastore['assetcol'] = self.assetcol
elif 'assetcol' in self.datastore.parent:
assetcol = self.datastore.parent['assetcol']
if oq.region:
region = wkt.loads(self.oqparam.region)
self.sitecol = haz_sitecol.within(region)
if oq.shakemap_id or 'shakemap' in oq.inputs:
self.sitecol, self.assetcol = self.read_shakemap(
haz_sitecol, assetcol)
self.datastore['assetcol'] = self.assetcol
logging.info('Extracted %d/%d assets', len(self.assetcol),
len(assetcol))
elif hasattr(self, 'sitecol') and general.not_equal(
self.sitecol.sids, haz_sitecol.sids):
self.assetcol = assetcol.reduce(self.sitecol)
self.datastore['assetcol'] = self.assetcol
logging.info('Extracted %d/%d assets', len(self.assetcol),
len(assetcol))
else:
self.assetcol = assetcol
else: # no exposure
self.sitecol = haz_sitecol
logging.info('Read %d hazard sites', len(self.sitecol))
if oq_hazard:
parent = self.datastore.parent
if 'assetcol' in parent:
check_time_event(oq, parent['assetcol'].occupancy_periods)
if oq_hazard.time_event and oq_hazard.time_event != oq.time_event:
raise ValueError(
'The risk configuration file has time_event=%s but the '
'hazard was computed with time_event=%s' %
(oq.time_event, oq_hazard.time_event))
if self.oqparam.job_type == 'risk':
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
# check that we are covering all the taxonomies in the exposure
missing = taxonomies - set(self.riskmodel.taxonomies)
if self.riskmodel and missing:
raise RuntimeError('The exposure contains the taxonomies %s '
'which are not in the risk model' % missing)
# same check for the consequence models, if any
consequence_models = riskmodels.get_risk_models(
self.oqparam, 'consequence')
for lt, cm in consequence_models.items():
missing = taxonomies - set(cm)
if missing:
raise ValueError('Missing consequenceFunctions for %s' %
' '.join(missing))
if hasattr(self, 'sitecol'):
self.datastore['sitecol'] = self.sitecol.complete
self.param = {} # used in the risk calculators
def _read_risk_data(self):
# read the exposure (if any), the risk model (if any) and then the
# site collection, possibly extracted from the exposure.
oq = self.oqparam
self.load_riskmodel() # must be called first
if oq.hazard_calculation_id:
with util.read(oq.hazard_calculation_id) as dstore:
haz_sitecol = dstore['sitecol'].complete
else:
haz_sitecol = readinput.get_site_collection(oq)
if hasattr(self, 'rup'):
# for scenario we reduce the site collection to the sites
# within the maximum distance from the rupture
haz_sitecol, _dctx = self.cmaker.filter(
haz_sitecol, self.rup)
haz_sitecol.make_complete()
if 'site_model' in oq.inputs:
self.datastore['site_model'] = readinput.get_site_model(oq)
oq_hazard = (self.datastore.parent['oqparam']
if self.datastore.parent else None)
if 'exposure' in oq.inputs:
exposure = self.read_exposure(haz_sitecol)
self.datastore['assetcol'] = self.assetcol
self.datastore['assetcol/num_taxonomies'] = (
self.assetcol.num_taxonomies_by_site())
if hasattr(readinput.exposure, 'exposures'):
self.datastore['assetcol/exposures'] = (
numpy.array(exposure.exposures, hdf5.vstr))
elif 'assetcol' in self.datastore.parent:
assetcol = self.datastore.parent['assetcol']
if oq.region:
region = wkt.loads(oq.region)
self.sitecol = haz_sitecol.within(region)
if oq.shakemap_id or 'shakemap' in oq.inputs:
self.sitecol, self.assetcol = self.read_shakemap(
haz_sitecol, assetcol)
self.datastore['assetcol'] = self.assetcol
logging.info('Extracted %d/%d assets',
len(self.assetcol), len(assetcol))
nsites = len(self.sitecol)
if (oq.spatial_correlation != 'no' and
nsites > MAXSITES): # hard-coded, heuristic
raise ValueError(CORRELATION_MATRIX_TOO_LARGE % nsites)
elif hasattr(self, 'sitecol') and general.not_equal(
self.sitecol.sids, haz_sitecol.sids):
self.assetcol = assetcol.reduce(self.sitecol)
self.datastore['assetcol'] = self.assetcol
self.datastore['assetcol/num_taxonomies'] = (
self.assetcol.num_taxonomies_by_site())
logging.info('Extracted %d/%d assets',
len(self.assetcol), len(assetcol))
else:
self.assetcol = assetcol
else: # no exposure
self.sitecol = haz_sitecol
if self.sitecol:
logging.info('Read %d hazard sites', len(self.sitecol))
if oq_hazard:
parent = self.datastore.parent
if 'assetcol' in parent:
check_time_event(oq, parent['assetcol'].occupancy_periods)
elif oq.job_type == 'risk' and 'exposure' not in oq.inputs:
raise ValueError('Missing exposure both in hazard and risk!')
if oq_hazard.time_event and oq_hazard.time_event != oq.time_event:
raise ValueError(
'The risk configuration file has time_event=%s but the '
'hazard was computed with time_event=%s' % (
oq.time_event, oq_hazard.time_event))
if oq.job_type == 'risk':
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
# check that we are covering all the taxonomies in the exposure
missing = taxonomies - set(self.riskmodel.taxonomies)
if self.riskmodel and missing:
raise RuntimeError('The exposure contains the taxonomies %s '
'which are not in the risk model' % missing)
# same check for the consequence models, if any
consequence_models = riskmodels.get_risk_models(
oq, 'consequence')
for lt, cm in consequence_models.items():
missing = taxonomies - set(cm)
if missing:
raise ValueError(
'Missing consequenceFunctions for %s' %
' '.join(missing))
if hasattr(self, 'sitecol') and self.sitecol:
self.datastore['sitecol'] = self.sitecol.complete
# used in the risk calculators
self.param = dict(individual_curves=oq.individual_curves,
avg_losses=oq.avg_losses)
# store the `exposed_value` if there is an exposure
if 'exposed_value' not in set(self.datastore) and hasattr(
self, 'assetcol'):
self.datastore['exposed_value'] = self.assetcol.agg_value(
*oq.aggregate_by)
def read_risk_data(self):
"""
Read the exposure (if any), the risk model (if any) and then the
site collection, possibly extracted from the exposure.
"""
oq = self.oqparam
with self.monitor('reading site collection', autoflush=True):
if oq.hazard_calculation_id:
with datastore.read(oq.hazard_calculation_id) as dstore:
haz_sitecol = dstore['sitecol'].complete
else:
haz_sitecol = readinput.get_site_collection(oq)
logging.info('There are %d hazard site(s)', len(haz_sitecol))
oq_hazard = (self.datastore.parent['oqparam']
if self.datastore.parent else None)
if 'exposure' in oq.inputs:
self.read_exposure(haz_sitecol)
self.load_riskmodel() # must be called *after* read_exposure
self.datastore['assetcol'] = self.assetcol
elif 'assetcol' in self.datastore.parent:
if self.oqparam.region_constraint:
region = wkt.loads(self.oqparam.region_constraint)
self.sitecol = haz_sitecol.within(region)
assetcol = self.datastore.parent['assetcol']
self.assetcol = assetcol.reduce(self.sitecol.sids)
self.datastore['assetcol'] = self.assetcol
logging.info('There are %d/%d assets in the region',
len(self.assetcol), len(assetcol))
else:
self.assetcol = self.datastore.parent['assetcol']
self.load_riskmodel()
else: # no exposure
self.load_riskmodel()
self.sitecol = haz_sitecol
if oq_hazard:
parent = self.datastore.parent
if 'assetcol' in parent:
check_time_event(oq, parent['assetcol'].occupancy_periods)
if oq_hazard.time_event and oq_hazard.time_event != oq.time_event:
raise ValueError(
'The risk configuration file has time_event=%s but the '
'hazard was computed with time_event=%s' %
(oq.time_event, oq_hazard.time_event))
if self.oqparam.job_type == 'risk':
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
# check that we are covering all the taxonomies in the exposure
missing = taxonomies - set(self.riskmodel.taxonomies)
if self.riskmodel and missing:
raise RuntimeError('The exposure contains the taxonomies %s '
'which are not in the risk model' % missing)
# same check for the consequence models, if any
consequence_models = riskmodels.get_risk_models(
self.oqparam, 'consequence')
for lt, cm in consequence_models.items():
missing = taxonomies - set(cm)
if missing:
raise ValueError('Missing consequenceFunctions for %s' %
' '.join(missing))
