def test_calculation_addition_args(self):
avg_periods = [0.05, 0.15, 1.0, 2.0, 4.0]
gmm = GenericGmpeAvgSA(gmpe_name="KothaEtAl2020ESHM20",
avg_periods=avg_periods,
corr_func="akkar",
sigma_mu_epsilon=1.0)
rctx = RuptureContext()
rctx.mag = 6.
rctx.hypo_depth = 15.
dctx = DistancesContext()
dctx.rjb = np.array([1., 10., 30., 70.])
sctx = SitesContext()
sctx.vs30 = 500.0 * np.ones(4)
sctx.vs30measured = np.ones(4, dtype="bool")
sctx.region = np.zeros(4, dtype=int)
stdt = [const.StdDev.TOTAL]
expected_mean = np.array(
[-1.72305707, -2.2178751, -3.20100306, -4.19948242])
expected_stddev = np.array(
[0.5532021, 0.5532021, 0.5532021, 0.5532021])
imtype = imt.AvgSA()
mean, [stddev] = gmm.get_mean_and_stddevs(sctx, rctx, dctx, imtype,
stdt)
np.testing.assert_almost_equal(mean, expected_mean)
np.testing.assert_almost_equal(stddev, expected_stddev)
def test_calculation_addition_args(self):
avg_periods = [0.05, 0.15, 1.0, 2.0, 4.0]
gmm = GenericGmpeAvgSA(gmpe_name="KothaEtAl2019SERA",
avg_periods=avg_periods,
corr_func="akkar",
sigma_mu_epsilon=1.0)
rctx = RuptureContext()
rctx.mag = 6.
rctx.hypo_depth = 15.
dctx = DistancesContext()
dctx.rjb = np.array([1., 10., 30., 70.])
sctx = SitesContext()
sctx.vs30 = 500.0 * np.ones(4)
sctx.vs30measured = np.ones(4, dtype="bool")
stdt = [const.StdDev.TOTAL]
expected_mean = np.array(
[-1.45586338, -1.94419233, -2.91884965, -3.91919928])
expected_stddev = np.array(
[0.58317566, 0.58317566, 0.58317566, 0.58317566])
imtype = imt.AvgSA()
mean, [stddev] = gmm.get_mean_and_stddevs(sctx, rctx, dctx, imtype,
stdt)
np.testing.assert_almost_equal(mean, expected_mean)
np.testing.assert_almost_equal(stddev, expected_stddev)
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 test01(self):
filename = os.path.join(DATA_PATH, 'kappa.txt')
gmm = AlAtikSigmaModel(gmpe_name='BindiEtAl2014Rjb',
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.])
imt = SA(0.1)
stdt = [const.StdDev.TOTAL]
mean_expected, stds_expected = gmm.get_mean_and_stddevs(
sites, rup, dists, imt, stdt)
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_calculationBA08_vs30variable(self):
""" Test mean and std calculation - BA08 - Vs30 variable"""
# Modified gmpe
mgmpe = NRCan15SiteTerm(gmpe_name='BooreAtkinson2008')
# Set parameters
sites = Dummy.get_site_collection(3, vs30=[400., 600, 1000])
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rjb = np.array([10., 10., 10.])
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 = BooreAtkinson2008()
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[:-1], mean_expected[:-1])
np.testing.assert_almost_equal(stds[:-1], stds_expected[:-1])
def test_gm_calculationBA08_vs30variable(self):
""" Test mean and std calculation - BA08 - Vs30 variable"""
# Modified gmpe
mgmpe = NRCan15SiteTerm(gmpe_name='BooreAtkinson2008')
# Set parameters
sites = Dummy.get_site_collection(3, vs30=[400., 600, 1000])
rup = Dummy.get_rupture(mag=6.0)
dists = DistancesContext()
dists.rjb = np.array([10., 10., 10.])
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 = BooreAtkinson2008()
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[:-1], mean_expected[:-1])
np.testing.assert_almost_equal(stds[:-1], stds_expected[:-1])
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 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