def test_gm_calculation_hard_rock(self):
""" Test mean and std calculation - on hard rock using AB06"""
# Modified gmpe
mgmpe = NRCan15SiteTermLinear(gmpe_name='AtkinsonBoore2006')
# Set parameters
sites = Dummy.get_site_collection(2, vs30=[760, 2010])
rup = Dummy.get_rupture(mag=7.0)
dists = DistancesContext()
dists.rrup = np.array([15., 15.])
stdt = [const.StdDev.TOTAL]
gmpe = AtkinsonBoore2006()
for imt in [PGA(), SA(1.0), SA(5.0)]:
#
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt,
stdt)
# Compute the expected results
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives
# the same results of the original gmpe
np.testing.assert_allclose(np.exp(mean),
np.exp(mean_expected),
rtol=1.0e-1)
np.testing.assert_allclose(stds, stds_expected)
def test_gm_calculation_soil_reference(self):
""" Test mean and std calculation - CY14 on reference rock"""
# Modified gmpe
mgmpe = CY14SiteTerm(gmpe_name='ChiouYoungs2014')
# Set parameters
sites = Dummy.get_site_collection(4,
vs30=1130.,
vs30measured=True,
z1pt0=0.)
rup = Dummy.get_rupture(mag=6.0)
rup.dip = 90.
rup.ztor = 0.
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
dists.rx = np.array([1., 10., 30., 70.])
dists.rjb = np.array([1., 10., 30., 70.])
imt = PGA()
stdt = [const.StdDev.TOTAL]
# Compute results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
# Compute the expected results
gmpe = ChiouYoungs2014()
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives the
# same results of the original gmpe
np.testing.assert_almost_equal(mean, mean_expected)
np.testing.assert_almost_equal(stds, stds_expected)
def test_gm_calculation_hard_rock(self):
""" Test mean and std calculation - on hard rock using AB06"""
# Modified gmpe
mgmpe = NRCan15SiteTermLinear(gmpe_name='AtkinsonBoore2006')
# Set parameters
sites = Dummy.get_site_collection(2, vs30=[760, 2010])
rup = Dummy.get_rupture(mag=7.0)
dists = DistancesContext()
dists.rrup = np.array([15., 15.])
stdt = [const.StdDev.TOTAL]
gmpe = AtkinsonBoore2006()
for imt in [PGA(), SA(1.0), SA(5.0)]:
#
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt,
stdt)
# Compute the expected results
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(sites,
rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives
# the same results of the original gmpe
np.testing.assert_allclose(np.exp(mean), np.exp(mean_expected),
rtol=1.0e-1)
np.testing.assert_allclose(stds, stds_expected)
def test02(self):
# checks if gmpe is always being evaluated at vs30=760
# see HID 2.6.2
filename = os.path.join(DATA_PATH, 'kappa.txt')
gmm = AlAtikSigmaModel(gmpe_name='YenierAtkinson2015ACME2019',
kappa_file=filename,
kappa_val='high')
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rjb = np.array([1., 10., 30., 70.])
dists.rrup = np.array([1., 10., 30., 70.])
imt = SA(0.1)
stdt = [const.StdDev.TOTAL]
mean_760, _ = gmm.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
sites2 = Dummy.get_site_collection(4, vs30=1500.)
mean_1500, _ = gmm.get_mean_and_stddevs(sites2, rup, dists, imt, stdt)
self.assertAlmostEqual(mean_760[-1], mean_1500[-1], 4)
def test_gm_calculationAB06_hard_bedrock(self):
""" Test mean and std calculation - AB06 on vs30 hard bedrock"""
# Modified gmpe
mgmpe = NRCan15SiteTerm(gmpe_name='AtkinsonBoore2006')
# Set parameters
sites = Dummy.get_site_collection(4, vs30=1999.0)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rrup = np.array([10., 20., 30., 40.])
imt = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
# Compute the expected results
gmpe = AtkinsonBoore2006()
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
# Test that for reference soil conditions the modified GMPE gives the
# same results of the original gmpe
np.testing.assert_almost_equal(mean, mean_expected)
np.testing.assert_almost_equal(stds, stds_expected)
def _get_stds(self, within_absolute=None, between_absolute=None):
if within_absolute is not None:
gmm = SplitSigmaGMPE(gmpe_name='Campbell2003',
within_absolute=within_absolute)
elif between_absolute is not None:
gmm = SplitSigmaGMPE(gmpe_name='Campbell2003',
between_absolute=between_absolute)
else:
raise ValueError('Unknown option')
# Set parameters
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
imt = PGA()
stds_types = [const.StdDev.TOTAL, const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT]
# Compute results
mean, stds = gmm.get_mean_and_stddevs(sites, rup, dists, imt,
stds_types)
return stds, stds_types
def test01(self):
avg_periods = [0.05, 0.15, 1.0, 2.0, 4.0]
gmm = gsim.mgmpe.generic_gmpe_avgsa.GenericGmpeAvgSA(
gmpe_name='ZhaoEtAl2006Asc',
avg_periods=avg_periods,
corr_func='akkar')
msg = 'The class name is incorrect'
self.assertTrue(gmm.__class__.__name__ == 'GenericGmpeAvgSA', msg=msg)
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
rup.hypo_depth = 10.
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
imtype = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, _ = gmm.get_mean_and_stddevs(sites, rup, dists, imtype, stdt)
expected = np.array(
[-1.33735637, -2.62649473, -3.64500654, -4.60067093])
np.testing.assert_almost_equal(mean, expected)
def test_gm_calculationAB06_hard_bedrock(self):
""" Test mean and std calculation - AB06 on vs30 hard bedrock"""
# Modified gmpe
mgmpe = NRCan15SiteTerm(gmpe_name='AtkinsonBoore2006')
# Set parameters
sites = Dummy.get_site_collection(4, vs30=1999.0)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rrup = np.array([10., 20., 30., 40.])
imt = PGA()
stdt = [const.StdDev.TOTAL]
# Computes results
mean, stds = mgmpe.get_mean_and_stddevs(sites, rup, dists, imt, stdt)
# Compute the expected results
gmpe = AtkinsonBoore2006()
mean_expected, stds_expected = gmpe.get_mean_and_stddevs(sites, rup,
dists, imt,
stdt)
# Test that for reference soil conditions the modified GMPE gives the
# same results of the original gmpe
np.testing.assert_almost_equal(mean, mean_expected)
np.testing.assert_almost_equal(stds, stds_expected)
def setUp(self):
# Set parameters - Setting z1pt0 does not make sense but here we
# want to make sure that the modified gmm provided GM amplified
# by the site term exactly as the original model.
sites = Dummy.get_site_collection(4,
vs30=400.,
vs30measured=True,
z1pt0=0.)
rup = Dummy.get_rupture(mag=6.0)
rup.dip = 90.
rup.ztor = 0.
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
dists.rx = np.array([1., 10., 30., 70.])
dists.rjb = np.array([1., 10., 30., 70.])
stdt = [const.StdDev.TOTAL]
self.rup = rup
self.dists = dists
self.stdt = stdt
self.sites = sites
def _get_stds(self, within_absolute=None, between_absolute=None):
if within_absolute is not None:
gmm = SplitSigmaGMPE(gmpe_name='Campbell2003',
within_absolute=within_absolute)
elif between_absolute is not None:
gmm = SplitSigmaGMPE(gmpe_name='Campbell2003',
between_absolute=between_absolute)
else:
raise ValueError('Unknown option')
# Set parameters
sites = Dummy.get_site_collection(4, vs30=760.)
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rrup = np.array([1., 10., 30., 70.])
imt = PGA()
stds_types = [
const.StdDev.TOTAL, const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT
]
# Compute results
mean, stds = gmm.get_mean_and_stddevs(sites, rup, dists, imt,
stds_types)
return stds, stds_types
def extrapolate_in_PSA(self, sites, rup, dists, imt_high, set_imt,
stds_types, imt):
extrap_mean = []
t_log10 = np.log10([im for im in set_imt])
for d in np.arange(0, len(dists.rjb)):
dist = DistancesContext()
if hasattr(dists, 'rjb'):
dist.rjb = np.array([dists.rjb[d]])
if hasattr(dists, 'rrup'):
dist.rrup = np.array([dists.rrup[d]])
means_log10 = []
for im in set_imt:
mean_ln, _ = self.gmpe.get_mean_and_stddevs(
sites, rup, dist, SA(im), stds_types)
mean = np.exp(mean_ln[0])
means_log10.append(np.log10(mean))
mb = np.polyfit(t_log10, means_log10, 1)
mean_imt_log10 = mb[0] * np.log10(imt) + mb[1]
extrap_mean.append(np.log(10**mean_imt_log10))
return extrap_mean
