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_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_Baker_Jayaram(self):
DATA_FILE = data / 'GENERIC_GMPE_AVGSA_MEAN_STD_TOTAL_BAKER_JAYARAM.csv'
# Initialise meta-GMPE
mgmpe = gsim.mgmpe.generic_gmpe_avgsa.GenericGmpeAvgSA(
gmpe_name='BooreAtkinson2008',
avg_periods=[0.05, 0.15, 1.0, 2.0, 4.0],
corr_func='baker_jayaram')
ctx = RuptureContext()
ctx.sids = [0]
P = imt.AvgSA
S = [const.StdDev.TOTAL]
with open(DATA_FILE, 'r') as f:
# Skip header
for i in [1, 2, 3]:
f.readline()
for line in f:
arr = np.float_(line.strip().split(','))
# Setting ground motion attributes
ctx.mag = arr[0]
ctx.rjb = np.array([arr[1]])
ctx.rake = arr[2]
ctx.hypo_depth = arr[3]
ctx.vs30 = np.array([arr[4]])
# Compute ground motion
mean, stdv = mgmpe.get_mean_and_stddevs(ctx, ctx, ctx, P, S)
np.testing.assert_almost_equal(mean, arr[6])
np.testing.assert_almost_equal(stdv, arr[7])