def site_term(self, ctx, dists, imt):
"""
Site term as used to calculate site coefficients for NBCC2015:
For Vs30 > 760 m/s use log-log interpolation of the 760-to-2000
factor of AA13.
For Vs30 < 760 m/s use the site term of Boore and Atkinson 2008.
Original site term is relative to Vs30 = 760m/s so needs to be made
relative to Vs30 = 450 m/s by dividing by the site term at Vs30 = 450.
Assume PGA_760 = 0.1g for Vs30 > 450 m/s. Also need to correct PGA at
site class C to 760 m/s. Cap PGA_450 at 0.1 - 0.5g.
"""
imls_pga = _return_tables(self, ctx.mag, PGA(), "IMLs")
PGA450 = _get_mean(self.kind, self.distance_type, imls_pga, ctx, dists)
imls_SA02 = _return_tables(self, ctx.mag, SA(0.2), "IMLs")
SA02 = _get_mean(self.kind, self.distance_type, imls_SA02, ctx, dists)
PGA450[SA02 / PGA450 < 2.0] = PGA450[SA02 / PGA450 < 2.0] * 0.8
pgas = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
pga_cors = np.array([0.083, 0.165, 0.248, 0.331, 0.414])
PGA760 = np.interp(PGA450, pgas, pga_cors)
PGA760_ref = np.copy(PGA760)
PGA760_ref[ctx.vs30 > 450.] = 0.1
vs30ref = np.zeros_like(ctx.vs30)
vs30ref += 450.0
C = self.COEFFS_2000_to_BC[imt]
site_term = (AB06_BA08(C, ctx.vs30, imt, PGA760_ref) /
AB06_BA08(C, vs30ref, imt, PGA760_ref))
return np.log(site_term)
def test_retreival_tables_outside_period_range(self):
"""
Tests that an error is raised when inputting a period value
outside the supported range
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
with self.assertRaises(ValueError) as ve:
_return_tables(gsim, 6.0, imt_module.SA(2.5), "IMLs")
self.assertEqual(
str(ve.exception),
"Spectral period 2.500 outside of valid range (0.100 to 2.000)")
def test_retreival_tables_outside_mag_range(self):
"""
Tests that an error is raised when inputting a magnitude value
outside the supported range
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
with self.assertRaises(ValueError) as ve:
_return_tables(gsim, 4.5, imt_module.PGA(), "IMLs")
self.assertEqual(
str(ve.exception),
"Magnitude 4.50 outside of supported range (5.00 to 7.00)")
def test_retreival_tables_good_interp(self):
"""
Tests the retreival of the IML tables for 'good' conditions with
magnitude interpolations
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
expected_table_pgv = np.array(
[midpoint(20., 40.),
midpoint(10., 20.),
midpoint(5., 10.)])
np.testing.assert_array_almost_equal(
_return_tables(gsim, 6.5, imt_module.PGV(), "IMLs"),
expected_table_pgv, 5)
expected_table_sa1 = np.array(
[midpoint(2., 4.),
midpoint(1., 2.),
midpoint(0.5, 1.)])
np.testing.assert_array_almost_equal(
_return_tables(gsim, 6.5, imt_module.SA(1.0), "IMLs"),
expected_table_sa1)
def get_hard_rock_mean(self, ctx, imt):
"""
Returns the mean and standard deviations for the reference very hard
rock condition (Vs30 = 3000 m/s)
"""
# Return Distance Tables
imls = _return_tables(self, ctx.mag, imt, "IMLs")
# Get distance vector for the given magnitude
idx = np.searchsorted(self.m_w, ctx.mag)
dists = self.distances[:, 0, idx - 1]
# Get mean and standard deviations
mean = _get_mean(self.kind, self.distance_type, imls, ctx, dists)
return np.log(mean)
def test_retreival_tables_good_no_interp(self):
"""
Tests the retreival of the IML tables for 'good' conditions without
applying magnitude interpolations
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
# PGA
np.testing.assert_array_almost_equal(
_return_tables(gsim, 6.0, imt_module.PGA(), "IMLs"),
np.array([2., 1., 0.5]))
# PGV
np.testing.assert_array_almost_equal(
_return_tables(gsim, 6.0, imt_module.PGV(), "IMLs"),
np.array([20., 10., 5.]), 5)
# SA(1.0)
np.testing.assert_array_almost_equal(
_return_tables(gsim, 6.0, imt_module.SA(1.0), "IMLs"),
np.array([2.0, 1., 0.5]))
# Also for standard deviations
np.testing.assert_array_almost_equal(
_return_tables(gsim, 6.0, imt_module.PGA(), 0), 0.5 * np.ones(3))
np.testing.assert_array_almost_equal(
_return_tables(gsim, 6.0, imt_module.SA(1.0), 0), 0.8 * np.ones(3))
def compute(self, ctx, imts, mean, sig, tau, phi):
"""
Returns the mean and standard deviations
"""
stds = [sig, tau, phi]
stdis = [
const.StdDev.idx[sdt]
for sdt in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
]
for m, imt in enumerate(imts):
# Return Distance Tables
imls = _return_tables(self, ctx.mag, imt, "IMLs")
# Get distance vector for the given magnitude
idx = np.searchsorted(self.m_w, ctx.mag)
dists = self.distances[:, 0, idx - 1]
# Get mean and standard deviations
mean[m] = np.log(
_get_mean(self.kind, self.distance_type, imls, ctx,
dists)) + site_term(self, ctx, dists, imt)
stddevs = _get_stddevs(self, dists, ctx, imt, stdis)
for s in stdis:
stds[s][m] = stddevs[s]
def get_mean_and_stddevs(self, sites, rx, dists, imt, stddev_types):
# List of GMPE weights, which is the product of the the branch weights
# for the seed models vs the NGA East resampled models as well as the
# weights for the indivudual GMPES as defined by Petersen et al. (2019)
#
# Note that the NGA East resampled models are a function of spectral
# period.
#
# NGA East Seeds (1/3)
# ├── B_bca10d (0.06633), wts = 0.333 * 0.06633 = 0.02208789
# ├── B_ab95 (0.02211), wts = 0.333 * 0.02211 = 0.00736263
# ...
# NGA East Resampled or "USGS" (2/3)
# ├── Model 1 (0.1009 for PGA), wts = 0.667 * 0.1009 = 0.0673003
# ├── Model 2 (0.1606 for PGA), wts = 0.667 * 0.1606 = 0.1071202
# ...
#
wts = [0] * len(self.gmpes)
# Is IMT PGA or PGV?
is_pga = imt == IMT.PGA()
is_pgv = imt == IMT.PGV()
# Is magnitude less than 4? If so, we will need to set it to 4.0 and
# then extrapolate the tables at the end.
# But... in the brave new world of new OpenQuake, sites, rx, and
# dists are all exactly the same object, so when we copy rx to
# rup, and change it, as well as when we change the distances
# below, we need to do it all in rup, then pass rup as all three
# contexts when we call the gmpe.get_mean_and_stddevs()
rup = copy.deepcopy(rx)
if rup.mag < 4.0:
is_small_mag = True
delta_mag = rup.mag - 4.0
rup.mag = 4.0
else:
is_small_mag = False
for i, tp in enumerate(self.ALL_TABLE_PATHS):
if 'usgs' in tp:
# Get model number from i-th path using regex
mod_num = int(re.search(r'\d+', tp).group())
coefs = np.array(self.NGA_EAST_USGS.iloc[mod_num - 1])
# Is the IMT PGA, PGA, or SA?
if is_pga:
iweight = coefs[-2]
elif is_pgv:
iweight = coefs[-1]
else:
# For SA, need to interpolate; we'll use log-period and
# linear-weight interpolation.
iweight = np.interp(
np.log(imt.period), np.log(self.per_array),
coefs[self.per_idx_start:self.per_idx_end])
wts[i] = self.NGA_EAST_USGS_WEIGHT * iweight
else:
# Strip off the cruft to get the string we need to match
str_match = tp.replace('nga_east_', '').replace('.hdf5', '')
matched = self.NGA_EAST_SEEDS[self.NGA_EAST_SEEDS['model'] ==
str_match]
if len(matched):
iweight = self.NGA_EAST_SEEDS[self.NGA_EAST_SEEDS['model']
== str_match].iloc[0, 1]
wts[i] = self.NGA_EAST_SEED_WEIGHT * iweight
total_gmpe_weights = self.sigma_weights * wts
if not np.allclose(np.sum(total_gmpe_weights), 1.0):
raise ValueError('Weights must sum to 1.0.')
mean = np.full_like(sites.vs30, 0)
stddevs = []
for i in range(len(stddev_types)):
stddevs.append(np.full_like(sites.vs30, 0))
# Apply max distance to dists.rrup -->> now rup.rrup
np.clip(rup.rrup, 0, MAX_RRUP)
#
# Some models don't have PGV terms, so we will make PSA for them
# and then use the conditional conversion to get PGV
#
if is_pgv:
ab2020 = AbrahamsonBhasin2020(rup.mag)
vimt = IMT.SA(ab2020.getTref())
# Loop over gmpes
for i, gm in enumerate(self.gmpes):
if is_pgv:
# Is PGV and also not available for gm?
try:
_ = _return_tables(gm, rup.mag, imt, "IMLs")
except KeyError:
#
# No table for PGV, compute vimt, then convert to PGV
#
vmean, vstddevs = gm.get_mean_and_stddevs(
rup, rup, rup, vimt, stddev_types)
tmean, tstddevs = ab2020.getPGVandSTDDEVS(
vmean, vstddevs, stddev_types, rup.rrup, rup.vs30)
except Exception:
logging.error("Unexpected error:", sys.exc_info()[0])
else:
#
# Table exists for PGV, proceed normally
#
tmean, tstddevs = gm.get_mean_and_stddevs(
rup, rup, rup, imt, stddev_types)
else:
tmean, tstddevs = gm.get_mean_and_stddevs(
rup, rup, rup, imt, stddev_types)
mean += tmean * total_gmpe_weights[i]
for j, sd in enumerate(tstddevs):
stddevs[j] += sd * total_gmpe_weights[i]
# Zero out values at distances beyond the range for which NGA East
# was defined. -->> was dists.rrup, now rup.rrup
mean[rup.rrup > MAX_RRUP] = -999.0
# Do we need to extrapolate for small magnitude factor?
if is_small_mag:
if is_pga:
slopes = np.interp(np.log(rup.rrup), np.log(self.SMALL_M_DIST),
self.SMALL_M_SLOPE_PGA)
elif is_pgv:
slopes = np.interp(np.log(rup.rrup), np.log(self.SMALL_M_DIST),
self.SMALL_M_SLOPE_PGV)
else:
interp_obj = RectBivariateSpline(np.log(self.SMALL_M_DIST),
np.log(self.SMALL_M_PER),
self.SMALL_M_SLOPE,
kx=1,
ky=1)
slopes = interp_obj.ev(np.log(rup.rrup), np.log(imt.period))
mean = mean + slopes * delta_mag
return mean, stddevs