def test_trivial(self):
# using the heaviside function, i.e. `amplify_one` has contributions
# only for soil_intensity < a * mid_intensity with a=1
# in this case the minimimum mid_intensity is 0.0015 which is
# smaller than the minimum soil intensity 0.0020, so some contribution
# is lost and this is the reason why the first poe in 0.985
# instead of 0.989
fname = gettemp(trivial_ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
a = Amplifier(self.imtls, df, self.soil_levels)
a.check(self.vs30, 0)
numpy.testing.assert_allclose(a.midlevels, [
0.0015, 0.0035, 0.0075, 0.015, 0.035, 0.075, 0.15, 0.35, 0.75, 1.1
])
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(
poes, [0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69], atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(
poes, [0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69], atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(
poes, [0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69], atol=1E-6)
def test_double(self):
fname = gettemp(double_ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
a = Amplifier(self.imtls, df, self.soil_levels)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.985122, 0.979701, 0.975965, 0.96634, 0.922497, 0.886351, 0.790249
],
atol=1E-6)
# poes, [0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79], atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.985122, 0.979701, 0.975965, 0.96634, 0.922497, 0.886351, 0.790249
],
atol=1E-6)
# poes, [0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79], atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.985122, 0.979701, 0.975965, 0.96634, 0.922497, 0.886351, 0.790249
],
atol=1E-6)
# poes, [0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79], atol=1E-6)
# amplify GMFs without sigmas
gmvs = a._amplify_gmvs(b'A', numpy.array([.1, .2, .3]), 'SA(0.5)')
numpy.testing.assert_allclose(gmvs, [.2, .4, .6])
def test_dupl(self):
fname = gettemp(dupl_ampl_func)
df = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64},
index='ampcode')
with self.assertRaises(ValueError) as ctx:
Amplifier(self.imtls, df, self.soil_levels)
self.assertEqual(str(ctx.exception), "Found duplicates for b'A'")
def test_simple(self):
fname = gettemp(simple_ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
a = Amplifier(self.imtls, df, self.soil_levels)
# a.check(self.vs30, vs30_tolerance=1)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.981141, 0.975771, 0.964955, 0.935616, 0.882413, 0.785659,
0.636667
],
atol=1e-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.981141, 0.975771, 0.964955, 0.935616, 0.882413, 0.785659,
0.636667
],
atol=1e-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.981681, 0.976563, 0.967238, 0.940109, 0.890456, 0.799286,
0.686047
],
atol=1e-6)
# Amplify GMFs with sigmas
numpy.random.seed(42)
gmvs = a._amplify_gmvs(b'A', numpy.array([.005, .010, .015]), 'PGA')
numpy.testing.assert_allclose(gmvs, [0.005401, 0.010356, 0.016704],
atol=1E-5)
def test_double(self):
fname = gettemp(double_ampl_func)
aw = read_csv(fname, {
'ampcode': 'S2',
'level': numpy.uint8,
None: numpy.float64
})
a = Amplifier(self.imtls, aw)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
def test_resampling(self):
path = os.path.dirname(os.path.abspath(__file__))
# Read AF
f_af = os.path.join(path, 'data', 'convolution', 'amplification.csv')
df_af = read_csv(f_af, {'ampcode': ampcode_dt, None: numpy.float64},
index='ampcode')
# Read hc
f_hc = os.path.join(path, 'data', 'convolution', 'hazard_curve.csv')
df_hc = pd.read_csv(f_hc, skiprows=1)
# Get imls from the hc
imls = []
pattern = 'poe-(\\d*\\.\\d*)'
for k in df_hc.columns:
m = re.match(pattern, k)
if m:
imls.append(float(m.group(1)))
imtls = DictArray({'PGA': imls})
# Create a list with one ProbabilityCurve instance
poes = numpy.squeeze(df_hc.iloc[0, 3:].to_numpy())
tmp = numpy.expand_dims(poes, 1)
pcurve = ProbabilityCurve(tmp)
soil_levels = numpy.array(list(numpy.geomspace(0.001, 2, 50)))
a = Amplifier(imtls, df_af, soil_levels)
res = a.amplify(b'MQ15', pcurve)
tmp = 'hazard_curve_expected.csv'
fname_expected = os.path.join(path, 'data', 'convolution', tmp)
expected = numpy.loadtxt(fname_expected)
numpy.testing.assert_allclose(numpy.squeeze(res.array), expected)
def test_double(self):
fname = gettemp(double_ampl_func)
aw = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64})
a = Amplifier(self.imtls, aw)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
# amplify GMFs without sigmas
gmvs = a._amplify_gmvs(b'A', numpy.array([.1, .2, .3]), 'SA(0.5)')
numpy.testing.assert_allclose(gmvs, [.2, .4, .6])
def test_simple(self):
fname = gettemp(simple_ampl_func)
aw = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64})
a = Amplifier(self.imtls, aw, self.soil_levels)
a.check(self.vs30, vs30_tolerance=1)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979997, 0.970004, 0.940069, 0.889961, 0.79, 0.690037],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979997, 0.970004, 0.940069, 0.889961, 0.79, 0.690037],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979996, 0.969991, 0.940012, 0.889958, 0.79, 0.690037],
atol=1E-6)
# amplify GMFs with sigmas
numpy.random.seed(42)
gmvs = a._amplify_gmvs(b'A', numpy.array([.005, .010, .015]), 'PGA')
numpy.testing.assert_allclose(gmvs, [0.005307, 0.010093, 0.016804],
atol=1E-5)
def test_gmf_with_uncertainty(self):
fname = gettemp(gmf_ampl_func)
aw = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64})
imtls = {'PGA': self.imls}
a = Amplifier(imtls, aw, self.soil_levels)
res = []
nsim = 10000
numpy.random.seed(42) # must be fixed
for i in range(nsim):
gmvs = a._amplify_gmvs(b'A', numpy.array([.1, .2, .3]), 'PGA')
res.append(list(gmvs))
res = numpy.array(res)
dat = numpy.reshape(numpy.tile([.1, .2, .3], nsim), (nsim, 3))
computed = numpy.std(numpy.log(res / dat), axis=0)
expected = numpy.array([0.3, 0.3, 0.3])
msg = "Computed and expected std do not match"
numpy.testing.assert_almost_equal(computed, expected, 2, err_msg=msg)
def test_gmf_cata(self):
fname = gettemp(cata_ampl_func)
df = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64},
index='ampcode')
imtls = DictArray({'PGA': [numpy.nan]})
a = Amplifier(imtls, df)
numpy.random.seed(42) # must be fixed
gmvs1 = a._amplify_gmvs(b'z1', numpy.array([.1, .2, .3]), 'PGA')
aac(gmvs1, [0.217124, 0.399295, 0.602515], atol=1E-5)
gmvs2 = a._amplify_gmvs(b'z2', numpy.array([.1, .2, .3]), 'PGA')
aac(gmvs2, [0.266652, 0.334187, 0.510845], atol=1E-5)
numpy.random.seed(43) # changing the seed the results change a lot
gmvs1 = a._amplify_gmvs(b'z1', numpy.array([.1, .2, .3]), 'PGA')
aac(gmvs1, [0.197304, 0.293422, 0.399669], atol=1E-5)
gmvs2 = a._amplify_gmvs(b'z2', numpy.array([.1, .2, .3]), 'PGA')
aac(gmvs2, [0.117069, 0.517284, 0.475571], atol=1E-5)
def calc_stats(self):
oq = self.oqparam
hstats = oq.hazard_stats()
# initialize datasets
N = len(self.sitecol.complete)
P = len(oq.poes)
M = len(oq.imtls)
if oq.soil_intensities is not None:
L = M * len(oq.soil_intensities)
else:
L = len(oq.imtls.array)
R = len(self.rlzs_assoc.realizations)
S = len(hstats)
if R > 1 and oq.individual_curves or not hstats:
self.datastore.create_dset('hcurves-rlzs', F32, (N, R, L))
if oq.poes:
self.datastore.create_dset('hmaps-rlzs', F32, (N, R, M, P))
if hstats:
self.datastore.create_dset('hcurves-stats', F32, (N, S, L))
if oq.poes:
self.datastore.create_dset('hmaps-stats', F32, (N, S, M, P))
ct = oq.concurrent_tasks
logging.info('Building hazard statistics with %d concurrent_tasks', ct)
weights = [rlz.weight for rlz in self.rlzs_assoc.realizations]
if 'amplification' in oq.inputs:
amplifier = Amplifier(oq.imtls, self.datastore['amplification'],
oq.soil_intensities)
amplifier.check(self.sitecol.vs30, oq.vs30_tolerance)
else:
amplifier = None
allargs = [ # this list is very fast to generate
(getters.PmapGetter(self.datastore, weights, t.sids, oq.poes), N,
hstats, oq.individual_curves, oq.max_sites_disagg, amplifier)
for t in self.sitecol.split_in_tiles(ct)
]
self.datastore.swmr_on()
parallel.Starmap(build_hazard, allargs,
h5=self.datastore.hdf5).reduce(self.save_hazard)
def test_simple(self):
#
# MP: checked using hand calculations some values of the poes computed
# considering uncertainty
#
fname = gettemp(simple_ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
a = Amplifier(self.imtls, df, self.soil_levels)
a.check(self.vs30, vs30_tolerance=1)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.985008, 0.980001, 0.970019, 0.94006, 0.890007, 0.790198, 0.690201
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.985008, 0.980001, 0.970019, 0.94006, 0.890007, 0.790198, 0.690201
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.985109, 0.980022, 0.970272, 0.940816, 0.890224, 0.792719,
0.692719
],
atol=1E-6)
# Amplify GMFs with sigmas
numpy.random.seed(42)
gmvs = a._amplify_gmvs(b'A', numpy.array([.005, .010, .015]), 'PGA')
numpy.testing.assert_allclose(gmvs, [0.005401, 0.010356, 0.016704],
atol=1E-5)
def test_simple(self):
fname = gettemp(simple_ampl_func)
aw = read_csv(fname, {
'ampcode': 'S2',
'level': numpy.uint8,
None: numpy.float64
})
a = Amplifier(self.imtls, aw, self.soil_levels)
a.check(self.vs30, 1)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979997, 0.970004, 0.940069, 0.889961, 0.79, 0.690037],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979997, 0.970004, 0.940069, 0.889961, 0.79, 0.690037],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979996, 0.969991, 0.940012, 0.889958, 0.79, 0.690037],
atol=1E-6)
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_crmodel() # must be called first
if oq.hazard_calculation_id:
with util.read(oq.hazard_calculation_id) as dstore:
haz_sitecol = dstore['sitecol'].complete
if ('amplification' in oq.inputs and
'ampcode' not in haz_sitecol.array.dtype.names):
haz_sitecol.add_col('ampcode', site.ampcode_dt)
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['cost_calculator'] = exposure.cost_calculator
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
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 N=%d hazard sites and L=%d hazard levels',
len(self.sitecol), len(oq.imtls.array))
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':
tmap_arr, tmap_lst = logictree.taxonomy_mapping(
self.oqparam.inputs.get('taxonomy_mapping'),
self.assetcol.tagcol.taxonomy)
self.crmodel.tmap = tmap_lst
if len(tmap_arr):
self.datastore['taxonomy_mapping'] = tmap_arr
taxonomies = set(taxo for items in self.crmodel.tmap
for taxo, weight in items if taxo != '?')
# check that we are covering all the taxonomies in the exposure
missing = taxonomies - set(self.crmodel.taxonomies)
if self.crmodel and missing:
raise RuntimeError('The exposure contains the taxonomies %s '
'which are not in the risk model' % missing)
if len(self.crmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.crmodel.taxonomies), len(taxonomies))
self.crmodel = self.crmodel.reduce(taxonomies)
self.crmodel.tmap = tmap_lst
self.crmodel.vectorize_cons_model(self.assetcol.tagcol)
if hasattr(self, 'sitecol') and self.sitecol:
if 'site_model' in oq.inputs:
assoc_dist = (oq.region_grid_spacing * 1.414
if oq.region_grid_spacing else 5) # Graeme's 5km
sm = readinput.get_site_model(oq)
self.sitecol.complete.assoc(sm, assoc_dist)
self.datastore['sitecol'] = self.sitecol.complete
# store amplification functions if any
if 'amplification' in oq.inputs:
logging.info('Reading %s', oq.inputs['amplification'])
self.datastore['amplification'] = readinput.get_amplification(oq)
check_amplification(self.datastore)
self.amplifier = Amplifier(
oq.imtls, self.datastore['amplification'], oq.soil_intensities)
self.amplifier.check(self.sitecol.vs30, oq.vs30_tolerance)
else:
self.amplifier = None
# used in the risk calculators
self.param = dict(individual_curves=oq.individual_curves,
avg_losses=oq.avg_losses, amplifier=self.amplifier)
# compute exposure stats
if hasattr(self, 'assetcol'):
save_exposed_values(
self.datastore, self.assetcol, oq.loss_names, oq.aggregate_by)
def _read_risk_data(self):
# read the risk model (if any), the exposure (if any) and then the
# site collection, possibly extracted from the exposure.
oq = self.oqparam
self.load_crmodel() # must be called first
if (not oq.imtls and 'shakemap' not in oq.inputs
and oq.ground_motion_fields):
raise InvalidFile('There are no intensity measure types in %s' %
oq.inputs['job_ini'])
if oq.hazard_calculation_id:
with util.read(oq.hazard_calculation_id) as dstore:
haz_sitecol = dstore['sitecol'].complete
if ('amplification' in oq.inputs
and 'ampcode' not in haz_sitecol.array.dtype.names):
haz_sitecol.add_col('ampcode', site.ampcode_dt)
else:
haz_sitecol = readinput.get_site_collection(oq, self.datastore)
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['cost_calculator'] = exposure.cost_calculator
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['sitecol'] = self.sitecol
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
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 and oq.imtls:
logging.info('Read N=%d hazard sites and L=%d hazard levels',
len(self.sitecol), oq.imtls.size)
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':
tmap_arr, tmap_lst = logictree.taxonomy_mapping(
self.oqparam.inputs.get('taxonomy_mapping'),
self.assetcol.tagcol.taxonomy)
self.crmodel.tmap = tmap_lst
if len(tmap_arr):
self.datastore['taxonomy_mapping'] = tmap_arr
taxonomies = set(taxo for items in self.crmodel.tmap
for taxo, weight in items if taxo != '?')
# check that we are covering all the taxonomies in the exposure
missing = taxonomies - set(self.crmodel.taxonomies)
if self.crmodel and missing:
raise RuntimeError('The exposure contains the taxonomies %s '
'which are not in the risk model' % missing)
if len(self.crmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.crmodel.taxonomies), len(taxonomies))
self.crmodel = self.crmodel.reduce(taxonomies)
self.crmodel.tmap = tmap_lst
self.crmodel.reduce_cons_model(self.assetcol.tagcol)
if hasattr(self, 'sitecol') and self.sitecol:
if 'site_model' in oq.inputs:
assoc_dist = (oq.region_grid_spacing *
1.414 if oq.region_grid_spacing else 5
) # Graeme's 5km
sm = readinput.get_site_model(oq)
self.sitecol.complete.assoc(sm, assoc_dist)
self.datastore['sitecol'] = self.sitecol
# store amplification functions if any
self.af = None
if 'amplification' in oq.inputs:
logging.info('Reading %s', oq.inputs['amplification'])
df = readinput.get_amplification(oq)
check_amplification(df, self.sitecol)
self.amplifier = Amplifier(oq.imtls, df, oq.soil_intensities)
if oq.amplification_method == 'kernel':
# TODO: need to add additional checks on the main calculation
# methodology since the kernel method is currently tested only
# for classical PSHA
self.af = AmplFunction.from_dframe(df)
self.amplifier = None
else:
self.amplifier = None
# manage secondary perils
sec_perils = oq.get_sec_perils()
for sp in sec_perils:
sp.prepare(self.sitecol) # add columns as needed
mal = {
lt: getdefault(oq.minimum_asset_loss, lt)
for lt in oq.loss_names
}
if mal:
logging.info('minimum_asset_loss=%s', mal)
self.param = dict(individual_curves=oq.individual_curves,
ps_grid_spacing=oq.ps_grid_spacing,
collapse_level=oq.collapse_level,
split_sources=oq.split_sources,
avg_losses=oq.avg_losses,
amplifier=self.amplifier,
sec_perils=sec_perils,
ses_seed=oq.ses_seed,
minimum_asset_loss=mal)
# compute exposure stats
if hasattr(self, 'assetcol'):
save_agg_values(self.datastore, self.assetcol, oq.loss_names,
oq.aggregate_by)
def test_dupl(self):
fname = gettemp(dupl_ampl_func)
aw = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64})
with self.assertRaises(ValueError):
Amplifier(self.imtls, aw)
