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_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])
def test_make_pmap(self):
trunclevel = 3
imtls = DictArray({'PGA': [0.01]})
gsims = [valid.gsim('AkkarBommer2010')]
ctxs = []
for occ_rate in (.001, .002):
ctx = RuptureContext()
ctx.mag = 5.5
ctx.rake = 90
ctx.occurrence_rate = occ_rate
ctx.sids = numpy.array([0.])
ctx.vs30 = numpy.array([760.])
ctx.rrup = numpy.array([100.])
ctx.rjb = numpy.array([99.])
ctxs.append(ctx)
pmap = make_pmap(ctxs, gsims, imtls, trunclevel, 50.)
numpy.testing.assert_almost_equal(pmap[0].array, 0.066381)
def test_get_pmap(self):
truncation_level = 3
imtls = DictArray({'PGA': [0.01]})
gsims = [valid.gsim('AkkarBommer2010')]
ctxs = []
for occ_rate in (.001, .002):
ctx = RuptureContext()
ctx.mag = 5.5
ctx.rake = 90
ctx.occurrence_rate = occ_rate
ctx.sids = numpy.array([0.])
ctx.vs30 = numpy.array([760.])
ctx.rrup = numpy.array([100.])
ctx.rjb = numpy.array([99.])
ctxs.append(ctx)
cmaker = ContextMaker(
'TRT', gsims, dict(imtls=imtls, truncation_level=truncation_level))
cmaker.tom = PoissonTOM(time_span=50)
pmap = cmaker.get_pmap(ctxs)
numpy.testing.assert_almost_equal(pmap[0].array, 0.066381)
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
ctx = RuptureContext()
ctx.mag = 6.0
ctx.sids = [0, 1, 2, 3]
ctx.vs30 = [760.] * 4
ctx.rrup = np.array([1., 10., 30., 70.])
ctx.occurrence_rate = .0001
imt = PGA()
stds_types = [const.StdDev.TOTAL, const.StdDev.INTER_EVENT,
const.StdDev.INTRA_EVENT]
# Compute results
mean, stds = gmm.get_mean_and_stddevs(ctx, ctx, ctx, imt, stds_types)
return stds, stds_types
def pt_src_are(self, pt_src, gsim, weight, lnSA, monitor):
"""
Returns the vector-valued Annual Rate of Exceedance for one single point-source
:param pt_src: single instance of class "openquake.hazardlib.source.area.PointSource"
:param gsim: tuple, containing (only one?) instance of Openquake GSIM class
:param: weight, weight to be multiplied to ARE estimate
:param lnSA: list, natural logarithm of acceleration values for each spectral period.
Note : Values should be ordered in the same order than self.periods
"""
annual_rate = 0
# Loop over ruptures:
# i.e. one rupture for each combination of (mag, nodal plane, hypocentral depth):
for r in pt_src.iter_ruptures():
# NOTE: IF ACCOUNTING FOR "pointsource_distance" IN THE INI FILE, ONE SHOULD USE THE
# "point_ruptures()" METHOD BELOW:
# Loop over ruptures, one rupture for each magnitude ( neglect floating and combination on
# nodal plane and hypocentral depth):
## for r in pt_src.point_ruptures():
# Note: Seismicity rate evenly distributed over all point sources
# Seismicity rate also accounts for FMD (i.e. decreasing for
# increasing magnitude value)
# Filter the site collection with respect to the rupture and prepare context objects:
context_maker = ContextMaker(r.tectonic_region_type, gsim)
site_ctx, dist_ctx = context_maker.make_contexts(self.sites, r)
rup_ctx = RuptureContext()
rup_ctx.mag = r.mag
rup_ctx.rake = r.rake
assert len(gsim)==1
annual_rate += r.occurrence_rate * weight * self.gm_poe(gsim[0],
dist_ctx,
rup_ctx,
site_ctx,
lnSA)
return annual_rate