def test_mag_greater_8pt5(self):
gmpe = SadighEtAl1997()
sctx = SitesContext()
rctx = RuptureContext()
dctx = DistancesContext()
rctx.rake = 0.0
dctx.rrup = numpy.array([0., 1.])
sctx.vs30 = numpy.array([800., 800.])
rctx.mag = 9.0
mean_rock_9, _ = gmpe.get_mean_and_stddevs(sctx, rctx, dctx, PGA(),
[StdDev.TOTAL])
rctx.mag = 8.5
mean_rock_8pt5, _ = gmpe.get_mean_and_stddevs(sctx, rctx, dctx, PGA(),
[StdDev.TOTAL])
numpy.testing.assert_allclose(mean_rock_9, mean_rock_8pt5)
sctx.vs30 = numpy.array([300., 300.])
rctx.mag = 9.0
mean_soil_9, _ = gmpe.get_mean_and_stddevs(sctx, rctx, dctx, PGA(),
[StdDev.TOTAL])
rctx.mag = 8.5
mean_soil_8pt5, _ = gmpe.get_mean_and_stddevs(sctx, rctx, dctx, PGA(),
[StdDev.TOTAL])
numpy.testing.assert_allclose(mean_soil_9, mean_soil_8pt5)
def test_mag_greater_8pt5(self):
gmpe = SadighEtAl1997()
sctx = SitesContext()
rctx = RuptureContext()
dctx = DistancesContext()
rctx.rake = 0.0
dctx.rrup = numpy.array([0., 1.])
sctx.vs30 = numpy.array([800., 800.])
rctx.mag = 9.0
mean_rock_9, _ = gmpe.get_mean_and_stddevs(
sctx, rctx, dctx, PGA(), [StdDev.TOTAL]
)
rctx.mag = 8.5
mean_rock_8pt5, _ = gmpe.get_mean_and_stddevs(
sctx, rctx, dctx, PGA(), [StdDev.TOTAL]
)
numpy.testing.assert_allclose(mean_rock_9, mean_rock_8pt5)
sctx.vs30 = numpy.array([300., 300.])
rctx.mag = 9.0
mean_soil_9, _ = gmpe.get_mean_and_stddevs(
sctx, rctx, dctx, PGA(), [StdDev.TOTAL]
)
rctx.mag = 8.5
mean_soil_8pt5, _ = gmpe.get_mean_and_stddevs(
sctx, rctx, dctx, PGA(), [StdDev.TOTAL]
)
numpy.testing.assert_allclose(mean_soil_9, mean_soil_8pt5)
def test_mag_dist_outside_range(self):
sctx = SitesContext()
rctx = RuptureContext()
dctx = DistancesContext()
# rupture with Mw = 3 (Mblg=2.9434938048208452) at rhypo = 1 must give
# same mean as rupture with Mw = 4.4 (Mblg=4.8927897867183798) at
# rhypo = 10
rctx.mag = 2.9434938048208452
dctx.rhypo = numpy.array([1])
mean_mw3_d1, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL])
rctx.mag = 4.8927897867183798
dctx.rhypo = numpy.array([10])
mean_mw4pt4_d10, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL])
self.assertAlmostEqual(float(mean_mw3_d1), float(mean_mw4pt4_d10))
# rupture with Mw = 9 (Mblg = 8.2093636421088814) at rhypo = 1500 km
# must give same mean as rupture with Mw = 8.2
# (Mblg = 7.752253535347597) at rhypo = 1000
rctx.mag = 8.2093636421088814
dctx.rhypo = numpy.array([1500.])
mean_mw9_d1500, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL])
rctx.mag = 7.752253535347597
dctx.rhypo = numpy.array([1000.])
mean_mw8pt2_d1000, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL])
self.assertAlmostEqual(mean_mw9_d1500, mean_mw8pt2_d1000)
def test_get_mean_table(self, idx=0):
"""
Test the retrieval of the mean amplification tables for a given
magnitude and IMT
"""
rctx = RuptureContext()
rctx.mag = 6.0
# PGA
expected_table = np.ones([10, 2])
expected_table[:, self.IDX] *= 1.5
np.testing.assert_array_almost_equal(
self.amp_table.get_mean_table(imt_module.PGA(), rctx),
expected_table)
# SA
expected_table[:, self.IDX] = 2.0 * np.ones(10)
np.testing.assert_array_almost_equal(
self.amp_table.get_mean_table(imt_module.SA(0.5), rctx),
expected_table)
# SA (period interpolation)
interpolator = interp1d(np.log10(self.amp_table.periods),
np.log10(np.array([1.5, 2.0, 0.5])))
period = 0.3
expected_table[:, self.IDX] = (
10.0 ** interpolator(np.log10(period))) * np.ones(10.)
np.testing.assert_array_almost_equal(
self.amp_table.get_mean_table(imt_module.SA(period), rctx),
expected_table)
def test_get_mean_and_stddevs(self):
"""
Tests mean and standard deviations without amplification
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
rctx = RuptureContext()
rctx.mag = 6.0
dctx = DistancesContext()
# Test values at the given distances and those outside range
dctx.rjb = np.array([0.5, 1.0, 10.0, 100.0, 500.0])
sctx = SitesContext()
stddevs = [const.StdDev.TOTAL]
expected_mean = np.array([2.0, 2.0, 1.0, 0.5, 1.0E-20])
# PGA
mean, sigma = gsim.get_mean_and_stddevs(sctx, rctx, dctx,
imt_module.PGA(),
stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], 0.5 * np.ones(5), 5)
# SA
mean, sigma = gsim.get_mean_and_stddevs(sctx, rctx, dctx,
imt_module.SA(1.0),
stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], 0.8 * np.ones(5), 5)
# PGV
mean, sigma = gsim.get_mean_and_stddevs(sctx, rctx, dctx,
imt_module.PGV(),
stddevs)
np.testing.assert_array_almost_equal(np.exp(mean),
10. * expected_mean,
5)
np.testing.assert_array_almost_equal(sigma[0], 0.5 * np.ones(5), 5)
def test_get_mean_and_stddevs_good(self):
"""
Tests the full execution of the GMPE tables for valid data
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
rctx = RuptureContext()
rctx.mag = 6.0
dctx = DistancesContext()
# Test values at the given distances and those outside range
dctx.rjb = np.array([0.5, 1.0, 10.0, 100.0, 500.0])
sctx = SitesContext()
sctx.vs30 = 1000. * np.ones(5)
stddevs = [const.StdDev.TOTAL]
expected_mean = np.array([2.0, 2.0, 1.0, 0.5, 1.0E-20])
expected_sigma = 0.25 * np.ones(5)
# PGA
mean, sigma = gsim.get_mean_and_stddevs(sctx, rctx, dctx,
imt_module.PGA(), stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], expected_sigma, 5)
# SA
mean, sigma = gsim.get_mean_and_stddevs(sctx, rctx, dctx,
imt_module.SA(1.0), stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], 0.4 * np.ones(5), 5)
# PGV
mean, sigma = gsim.get_mean_and_stddevs(sctx, rctx, dctx,
imt_module.PGV(), stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), 10. * expected_mean,
5)
np.testing.assert_array_almost_equal(sigma[0], expected_sigma, 5)
def test_get_mean_table(self, idx=0):
"""
Test the retrieval of the mean amplification tables for a given
magnitude and IMT
"""
ctx = RuptureContext()
ctx.mag = 6.0
# PGA
expected_table = np.ones([10, 2])
expected_table[:, self.IDX] *= 1.5
np.testing.assert_array_almost_equal(
self.amp_table.get_mean_table(imt_module.PGA(), ctx),
expected_table)
# SA
expected_table[:, self.IDX] = 2.0 * np.ones(10)
np.testing.assert_array_almost_equal(
self.amp_table.get_mean_table(imt_module.SA(0.5), ctx),
expected_table)
# SA (period interpolation)
interpolator = interp1d(np.log10(self.amp_table.periods),
np.log10(np.array([1.5, 2.0, 0.5])))
period = 0.3
expected_table[:, self.IDX] = (10.0**interpolator(
np.log10(period))) * np.ones(10)
np.testing.assert_array_almost_equal(
self.amp_table.get_mean_table(imt_module.SA(period), ctx),
expected_table)
def test_get_amplification_factors(self):
"""
Tests the amplification tables
"""
ctx = RuptureContext()
ctx.rake = 45.0
ctx.mag = 6.0
# Takes distances at the values found in the table (not checking
# distance interpolation)
ctx.rjb = np.copy(self.amp_table.distances[:, 0, 0])
# Test Vs30 is 700.0 m/s midpoint between the 400 m/s and 1000 m/s
# specified in the table
stddevs = [const.StdDev.TOTAL]
expected_mean = np.ones_like(ctx.rjb)
# Check PGA and PGV
mean_amp, sigma_amp = self.amp_table.get_amplification_factors(
imt_module.PGA(), ctx, ctx.rjb, stddevs)
np.testing.assert_array_almost_equal(
mean_amp,
midpoint(1.0, 1.5) * expected_mean)
np.testing.assert_array_almost_equal(sigma_amp[0], 0.9 * expected_mean)
mean_amp, sigma_amp = self.amp_table.get_amplification_factors(
imt_module.PGV(), ctx, ctx.rjb, stddevs)
np.testing.assert_array_almost_equal(
mean_amp,
midpoint(1.0, 0.5) * expected_mean)
np.testing.assert_array_almost_equal(sigma_amp[0], 0.9 * expected_mean)
# Sa (0.5)
mean_amp, sigma_amp = self.amp_table.get_amplification_factors(
imt_module.SA(0.5), ctx, ctx.rjb, stddevs)
np.testing.assert_array_almost_equal(
mean_amp,
midpoint(1.0, 2.0) * expected_mean)
np.testing.assert_array_almost_equal(sigma_amp[0], 0.9 * expected_mean)
def test_get_mean_and_stddevs_good_amplified(self):
"""
Tests the full execution of the GMPE tables for valid data with
amplification
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
ctx = RuptureContext()
ctx.mag = 6.0
# Test values at the given distances and those outside range
ctx.rjb = np.array([0.5, 1.0, 10.0, 100.0, 500.0])
ctx.sids = np.arange(5)
ctx.vs30 = 100. * np.ones(5)
stddevs = [const.StdDev.TOTAL]
expected_mean = np.array([20., 20., 10., 5., 1.0E-19])
expected_sigma = 0.25 * np.ones(5)
# PGA
mean, sigma = gsim.get_mean_and_stddevs(ctx, ctx, ctx,
imt_module.PGA(), stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], expected_sigma, 5)
# SA
mean, sigma = gsim.get_mean_and_stddevs(ctx, ctx, ctx,
imt_module.SA(1.0), stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], 0.4 * np.ones(5), 5)
def test_get_mean_and_stddevs(self):
"""
Tests mean and standard deviations without amplification
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
ctx = RuptureContext()
ctx.mag = 6.0
# Test values at the given distances and those outside range
ctx.rjb = np.array([0.5, 1.0, 10.0, 100.0, 500.0])
ctx.sids = np.arange(5)
stddevs = [const.StdDev.TOTAL]
expected_mean = np.array([2.0, 2.0, 1.0, 0.5, 1.0E-20])
# PGA
mean, sigma = gsim.get_mean_and_stddevs(ctx, ctx, ctx,
imt_module.PGA(), stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], 0.5 * np.ones(5), 5)
# SA
mean, sigma = gsim.get_mean_and_stddevs(ctx, ctx, ctx,
imt_module.SA(1.0), stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], 0.8 * np.ones(5), 5)
# PGV
mean, sigma = gsim.get_mean_and_stddevs(ctx, ctx, ctx,
imt_module.PGV(), stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), 10. * expected_mean,
5)
np.testing.assert_array_almost_equal(sigma[0], 0.5 * np.ones(5), 5)
def calculate_total_std(gsim_list, imts, vs30):
std_total = {}
std_inter = {}
std_intra = {}
for gsim in gsim_list:
rctx = RuptureContext()
# The calculator needs these inputs but they are not used
# in the std calculation
rctx.mag = 5
rctx.rake = 0
rctx.hypo_depth = 0
dctx = DistancesContext()
dctx.rjb = np.copy(np.array([1])) # I do not care about the distance
dctx.rrup = np.copy(np.array([1])) # I do not care about the distance
sctx = SitesContext()
sctx.vs30 = vs30 * np.ones_like(np.array([0]))
for imt in imts:
gm_table, [
gm_stddev_inter, gm_stddev_intra
] = (gsim.get_mean_and_stddevs(
sctx, rctx, dctx, imt,
[const.StdDev.INTER_EVENT, const.StdDev.INTRA_EVENT]))
std_total[gsim, imt] = (np.sqrt(gm_stddev_inter[0]**2 +
gm_stddev_intra[0]**2))
std_inter[gsim, imt] = gm_stddev_inter[0]
std_intra[gsim, imt] = gm_stddev_intra[0]
return (std_total, std_inter, std_intra)
def test_get_mean_and_stddevs_good(self):
"""
Tests the full execution of the GMPE tables for valid data
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
rctx = RuptureContext()
rctx.mag = 6.0
rctx.rake = 90.0
dctx = DistancesContext()
# Test values at the given distances and those outside range
dctx.rjb = np.array([0.5, 1.0, 10.0, 100.0, 500.0])
sctx = SitesContext()
stddevs = [const.StdDev.TOTAL]
expected_mean = np.array([20.0, 20.0, 10.0, 5.0, 1.0E-19])
# PGA
mean, sigma = gsim.get_mean_and_stddevs(sctx, rctx, dctx,
imt_module.PGA(),
stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], 0.25 * np.ones(5), 5)
# SA
mean, sigma = gsim.get_mean_and_stddevs(sctx, rctx, dctx,
imt_module.SA(1.0),
stddevs)
np.testing.assert_array_almost_equal(np.exp(mean), expected_mean, 5)
np.testing.assert_array_almost_equal(sigma[0], 0.4 * np.ones(5), 5)
def test_recarray_conversion(self):
# automatic recarray conversion for backward compatibility
imt = PGA()
gsim = AbrahamsonGulerce2020SInter()
ctx = RuptureContext()
ctx.mag = 5.
ctx.sids = [0, 1]
ctx.vs30 = [760., 760.]
ctx.rrup = [100., 110.]
mean, _stddevs = gsim.get_mean_and_stddevs(ctx, ctx, ctx, imt, [])
numpy.testing.assert_allclose(mean, [-5.81116004, -6.00192455])
def test_dist_not_in_increasing_order(self):
ctx = RuptureContext()
ctx.mag = 5.
ctx.sids = [0, 1]
ctx.rhypo = numpy.array([150, 100])
mean_150_100, _ = self.GSIM_CLASS().get_mean_and_stddevs(
ctx, ctx, ctx, SA(0.1, 5), [StdDev.TOTAL])
ctx.rhypo = numpy.array([100, 150])
mean_100_150, _ = self.GSIM_CLASS().get_mean_and_stddevs(
ctx, ctx, ctx, SA(0.1, 5), [StdDev.TOTAL])
self.assertAlmostEqual(mean_150_100[1], mean_100_150[0])
self.assertAlmostEqual(mean_150_100[0], mean_100_150[1])
def test_mag_dist_outside_range(self):
sctx = SitesContext()
rctx = RuptureContext()
dctx = DistancesContext()
# rupture with Mw = 3 (Mblg=2.9434938048208452) at rhypo = 1 must give
# same mean as rupture with Mw = 4.4 (Mblg=4.8927897867183798) at
# rhypo = 10
rctx.mag = 2.9434938048208452
dctx.rhypo = numpy.array([1])
mean_mw3_d1, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL]
)
rctx.mag = 4.8927897867183798
dctx.rhypo = numpy.array([10])
mean_mw4pt4_d10, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL]
)
self.assertAlmostEqual(float(mean_mw3_d1), float(mean_mw4pt4_d10))
# rupture with Mw = 9 (Mblg = 8.2093636421088814) at rhypo = 1500 km
# must give same mean as rupture with Mw = 8.2
# (Mblg = 7.752253535347597) at rhypo = 1000
rctx.mag = 8.2093636421088814
dctx.rhypo = numpy.array([1500.])
mean_mw9_d1500, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL]
)
rctx.mag = 7.752253535347597
dctx.rhypo = numpy.array([1000.])
mean_mw8pt2_d1000, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL]
)
self.assertAlmostEqual(mean_mw9_d1500, mean_mw8pt2_d1000)
def test_dist_not_in_increasing_order(self):
sctx = SitesContext()
rctx = RuptureContext()
dctx = DistancesContext()
rctx.mag = 5.
dctx.rhypo = numpy.array([150, 100])
mean_150_100, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL])
dctx.rhypo = numpy.array([100, 150])
mean_100_150, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL])
self.assertAlmostEqual(mean_150_100[1], mean_100_150[0])
self.assertAlmostEqual(mean_150_100[0], mean_100_150[1])
def test_zero_distance(self):
# test the calculation in case of zero rrup distance (for rrup=0
# the equations have a singularity). In this case the
# method should return values equal to the ones obtained by
# replacing 0 values with 1
ctx = RuptureContext()
ctx.sids = [0, 1]
ctx.vs30 = numpy.array([500.0, 2500.0])
ctx.mag = 5.0
ctx.rrup = numpy.array([0.0, 0.2])
mean_0, stds_0 = self.GSIM_CLASS().get_mean_and_stddevs(
ctx, ctx, ctx, PGA(), [StdDev.TOTAL])
ctx.rrup = numpy.array([1.0, 0.2])
mean_01, stds_01 = self.GSIM_CLASS().get_mean_and_stddevs(
ctx, ctx, ctx, PGA(), [StdDev.TOTAL])
numpy.testing.assert_array_equal(mean_0, mean_01)
numpy.testing.assert_array_equal(stds_0, stds_01)
def evaluate_model(site_params, rup_params, df, npts, azimuth, moveout, mod,
imt):
sx = SitesContext()
rx = RuptureContext()
dx = DistancesContext()
# TODO: some site parameters can be pulled from the dataframe so we don't
# have to use the defaults (vs30, azimuth, etc.)
if not moveout:
npts = df.shape[0]
for param in site_params.keys():
setattr(sx, param, np.full(npts, site_params[param]))
rx.__dict__.update(rup_params)
rx.mag = df['EarthquakeMagnitude'].iloc[0]
rx.hypo_depth = df['EarthquakeDepth'].iloc[0]
if moveout:
dx.rjb = np.linspace(0, df['JoynerBooreDistance'].max(), npts)
dx.rrup = np.sqrt(dx.rjb**2 + df['EarthquakeDepth'].iloc[0]**2)
dx.rhypo = dx.rrup
dx.repi = dx.rjb
else:
dx.rjb = df['JoynerBooreDistance']
dx.rrup = df['RuptureDistance']
dx.rhypo = df['HypocentralDistance']
dx.repi = df['EpicentralDistance']
# TODO: some of these distances can be pulled from the dataframe
dx.ry0 = dx.rjb
dx.rx = np.full_like(dx.rjb, -1)
dx.azimuth = np.full_like(npts, azimuth)
dx.rcdpp = dx.rjb
dx.rvolc = dx.rjb
try:
mean, sd = MODELS_DICT[mod]().get_mean_and_stddevs(
sx, rx, dx,
manage_imts(imt)[0], [StdDev.TOTAL])
mean = convert_units(mean, imt)
if moveout:
return mean, dx
else:
return mean, sd[0]
except Exception:
return
def test_dist_not_in_increasing_order(self):
sctx = SitesContext()
rctx = RuptureContext()
dctx = DistancesContext()
rctx.mag = 5.
dctx.rhypo = numpy.array([150, 100])
mean_150_100, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL]
)
dctx.rhypo = numpy.array([100, 150])
mean_100_150, _ = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, SA(0.1, 5), [StdDev.TOTAL]
)
self.assertAlmostEqual(mean_150_100[1], mean_100_150[0])
self.assertAlmostEqual(mean_150_100[0], mean_100_150[1])
def test_get_sigma_table(self):
"""
Test the retrieval of the standard deviation modification tables
for a given magnitude and IMT
"""
ctx = RuptureContext()
ctx.mag = 6.0
# PGA
expected_table = np.ones([10, 2])
expected_table[:, self.IDX] *= 0.8
stddevs = [const.StdDev.TOTAL]
pga_table = self.amp_table.get_sigma_tables(imt_module.PGA(), ctx,
stddevs)[0]
np.testing.assert_array_almost_equal(pga_table, expected_table)
# SA (for coverage)
sa_table = self.amp_table.get_sigma_tables(imt_module.SA(0.3), ctx,
stddevs)[0]
np.testing.assert_array_almost_equal(sa_table, expected_table)
def test_rhypo_smaller_than_15(self):
# test the calculation in case of rhypo distances less than 15 km
# (for rhypo=0 the distance term has a singularity). In this case the
# method should return values equal to the ones obtained by clipping
# distances at 15 km.
ctx = RuptureContext()
ctx.sids = [0, 1, 2]
ctx.vs30 = numpy.array([800.0, 800.0, 800.0])
ctx.mag = 5.0
ctx.rake = 0
ctx.rhypo = numpy.array([0.0, 10.0, 16.0])
ctx.rhypo.flags.writeable = False
mean_0, stds_0 = self.GSIM_CLASS().get_mean_and_stddevs(
ctx, ctx, ctx, PGA(), [StdDev.TOTAL])
mean_15, stds_15 = self.GSIM_CLASS().get_mean_and_stddevs(
ctx, ctx, ctx, PGA(), [StdDev.TOTAL])
numpy.testing.assert_array_equal(mean_0, mean_15)
numpy.testing.assert_array_equal(stds_0, stds_15)
def test_get_mean_stddevs_unsupported_stddev(self):
"""
Tests the execution of the GMPE with an unsupported standard deviation
type
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
rctx = RuptureContext()
rctx.mag = 6.0
dctx = DistancesContext()
# Test values at the given distances and those outside range
dctx.rjb = np.array([0.5, 1.0, 10.0, 100.0, 500.0])
sctx = SitesContext()
sctx.vs30 = 1000. * np.ones(5)
stddevs = [const.StdDev.TOTAL, const.StdDev.INTER_EVENT]
with self.assertRaises(ValueError) as ve:
gsim.get_mean_and_stddevs(sctx, rctx, dctx, imt_module.PGA(),
stddevs)
self.assertEqual(str(ve.exception),
"Standard Deviation type Inter event not supported")
def test_get_mean_stddevs_unsupported_stddev(self):
"""
Tests the execution of the GMPE with an unsupported standard deviation
type
"""
gsim = GMPETable(gmpe_table=self.TABLE_FILE)
rctx = RuptureContext()
rctx.mag = 6.0
dctx = DistancesContext()
# Test values at the given distances and those outside range
dctx.rjb = np.array([0.5, 1.0, 10.0, 100.0, 500.0])
sctx = SitesContext()
sctx.vs30 = 1000. * np.ones(5)
stddevs = [const.StdDev.TOTAL, const.StdDev.INTER_EVENT]
with self.assertRaises(ValueError) as ve:
gsim.get_mean_and_stddevs(sctx, rctx, dctx, imt_module.PGA(),
stddevs)
self.assertEqual(str(ve.exception),
"Standard Deviation type Inter event not supported")
def test_get_amplification_factors(self):
"""
Tests the amplification tables
"""
rctx = RuptureContext()
rctx.mag = 6.0
dctx = DistancesContext()
# Takes distances at the values found in the table (not checking
# distance interpolation)
dctx.rjb = np.copy(self.amp_table.distances[:, 0, 0])
# Test Vs30 is 700.0 m/s midpoint between the 400 m/s and 1000 m/s
# specified in the table
sctx = SitesContext()
sctx.vs30 = 700.0 * np.ones_like(dctx.rjb)
stddevs = [const.StdDev.TOTAL]
expected_mean = np.ones_like(dctx.rjb)
expected_sigma = np.ones_like(dctx.rjb)
# Check PGA and PGV
mean_amp, sigma_amp = self.amp_table.get_amplification_factors(
imt_module.PGA(), sctx, rctx, dctx.rjb, stddevs)
np.testing.assert_array_almost_equal(
mean_amp,
midpoint(1.0, 1.5) * expected_mean)
np.testing.assert_array_almost_equal(
sigma_amp[0],
0.9 * expected_mean)
mean_amp, sigma_amp = self.amp_table.get_amplification_factors(
imt_module.PGV(), sctx, rctx, dctx.rjb, stddevs)
np.testing.assert_array_almost_equal(
mean_amp,
midpoint(1.0, 0.5) * expected_mean)
np.testing.assert_array_almost_equal(
sigma_amp[0],
0.9 * expected_mean)
# Sa (0.5)
mean_amp, sigma_amp = self.amp_table.get_amplification_factors(
imt_module.SA(0.5), sctx, rctx, dctx.rjb, stddevs)
np.testing.assert_array_almost_equal(
mean_amp,
midpoint(1.0, 2.0) * expected_mean)
np.testing.assert_array_almost_equal(
sigma_amp[0],
0.9 * expected_mean)
def check_gmpe_adjustments(self, adj_gmpe_set, original_gmpe):
"""
Takes a set of three adjusted GMPEs representing the "low", "middle"
and "high" stress drop adjustments for Germany and compares them
against the original "target" GMPE for a variety of magnitudes
and styles of fauling.
"""
low_gsim, mid_gsim, high_gsim = adj_gmpe_set
tot_std = [const.StdDev.TOTAL]
for imt in self.imts:
for mag in self.mags:
for rake in self.rakes:
rctx = RuptureContext()
rctx.mag = mag
rctx.rake = rake
rctx.hypo_depth = 10.
# Get "original" values
mean = original_gmpe.get_mean_and_stddevs(self.sctx, rctx,
self.dctx, imt,
tot_std)[0]
mean = np.exp(mean)
# Get "low" adjustments (0.75 times the original)
low_mean = low_gsim.get_mean_and_stddevs(self.sctx, rctx,
self.dctx, imt,
tot_std)[0]
np.testing.assert_array_almost_equal(
np.exp(low_mean) / mean, 0.75 * np.ones_like(low_mean))
# Get "middle" adjustments (1.25 times the original)
mid_mean = mid_gsim.get_mean_and_stddevs(self.sctx, rctx,
self.dctx, imt,
tot_std)[0]
np.testing.assert_array_almost_equal(
np.exp(mid_mean) / mean, 1.25 * np.ones_like(mid_mean))
# Get "high" adjustments (1.5 times the original)
high_mean = high_gsim.get_mean_and_stddevs(self.sctx, rctx,
self.dctx, imt,
tot_std)[0]
np.testing.assert_array_almost_equal(
np.exp(high_mean) / mean,
1.5 * np.ones_like(high_mean))
def test_zero_distance(self):
# test the calculation in case of zero rrup distance (for rrup=0
# the slab correction term has a singularity). In this case the
# method should return values equal to the ones obtained by
# replacing 0 values with 0.1
ctx = RuptureContext()
ctx.sids = [0, 1]
ctx.vs30 = numpy.array([800.0, 800.0])
ctx.mag = 5.0
ctx.rake = 0.0
ctx.hypo_depth = 0.0
ctx.rrup = numpy.array([0.0, 0.2])
ctx.occurrence_rate = .0001
mean_0, stds_0 = self.GSIM_CLASS().get_mean_and_stddevs(
ctx, ctx, ctx, PGA(), [StdDev.TOTAL])
ctx.rrup = numpy.array([0.1, 0.2])
mean_01, stds_01 = self.GSIM_CLASS().get_mean_and_stddevs(
ctx, ctx, ctx, PGA(), [StdDev.TOTAL])
numpy.testing.assert_array_equal(mean_0, mean_01)
numpy.testing.assert_array_equal(stds_0, stds_01)
def test_rhypo_smaller_than_15(self):
# test the calculation in case of rhypo distances less than 15 km
# (for rhypo=0 the distance term has a singularity). In this case the
# method should return values equal to the ones obtained by clipping
# distances at 15 km.
sctx = SitesContext()
sctx.vs30 = numpy.array([800.0, 800.0, 800.0])
rctx = RuptureContext()
rctx.mag = 5.0
rctx.rake = 0
dctx = DistancesContext()
dctx.rhypo = numpy.array([0.0, 10.0, 16.0])
dctx.rhypo.flags.writeable = False
mean_0, stds_0 = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, PGA(), [StdDev.TOTAL])
setattr(dctx, 'rhypo', numpy.array([15.0, 15.0, 16.0]))
mean_15, stds_15 = self.GSIM_CLASS().get_mean_and_stddevs(
sctx, rctx, dctx, PGA(), [StdDev.TOTAL])
numpy.testing.assert_array_equal(mean_0, mean_15)
numpy.testing.assert_array_equal(stds_0, stds_15)
def get_response_spectrum(self, magnitude, distance, periods, rake=90, vs30=800, damping=0.05):
"""
"""
responses = np.zeros((len(periods),))
p_damping = damping * 100
rup = RuptureContext()
rup.mag = magnitude
rup.rake = rake
dists = DistancesContext()
dists.rjb = np.array([distance])
sites = SitesContext()
sites.vs30 = np.array([vs30])
stddev_types = [StdDev.TOTAL]
for i, period in enumerate(periods):
if period == 0:
imt = _PGA()
else:
imt = _SA(period, p_damping)
responses[i] = np.exp(self._gmpe.get_mean_and_stddevs(sites, rup, dists, imt, stddev_types)[0][0])
return ResponseSpectrum(periods, responses, unit='g', damping=damping)
def test_get_sigma_table(self):
"""
Test the retrieval of the standard deviation modification tables
for a given magnitude and IMT
"""
rctx = RuptureContext()
rctx.mag = 6.0
# PGA
expected_table = np.ones([10, 2])
expected_table[:, self.IDX] *= 0.8
stddevs = ["Total"]
pga_table = self.amp_table.get_sigma_tables(imt_module.PGA(),
rctx,
stddevs)[0]
np.testing.assert_array_almost_equal(pga_table, expected_table)
# SA (for coverage)
sa_table = self.amp_table.get_sigma_tables(imt_module.SA(0.3),
rctx,
stddevs)[0]
np.testing.assert_array_almost_equal(sa_table, expected_table)
def test_equality(self):
sctx1 = SitesContext()
sctx1.vs30 = numpy.array([500., 600., 700.])
sctx1.vs30measured = True
sctx1.z1pt0 = numpy.array([40., 50., 60.])
sctx1.z2pt5 = numpy.array([1, 2, 3])
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 == sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 != sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = False
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 != sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
self.assertTrue(sctx1 != sctx2)
rctx = RuptureContext()
rctx.mag = 5.
self.assertTrue(sctx1 != rctx)
def test_equality(self):
sctx1 = SitesContext()
sctx1.vs30 = numpy.array([500., 600., 700.])
sctx1.vs30measured = True
sctx1.z1pt0 = numpy.array([40., 50., 60.])
sctx1.z2pt5 = numpy.array([1, 2, 3])
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 == sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 != sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = False
sctx2.z1pt0 = numpy.array([40., 50., 60.])
sctx2.z2pt5 = numpy.array([1, 2, 3])
self.assertTrue(sctx1 != sctx2)
sctx2 = SitesContext()
sctx2.vs30 = numpy.array([500., 600., 700.])
sctx2.vs30measured = True
sctx2.z1pt0 = numpy.array([40., 50., 60.])
self.assertTrue(sctx1 != sctx2)
rctx = RuptureContext()
rctx.mag = 5.
self.assertTrue(sctx1 != rctx)
def check_gmpe_adjustments(self, adj_gmpe_set, original_gmpe):
"""
Takes a set of three adjusted GMPEs representing the "low", "middle"
and "high" stress drop adjustments for Germany and compares them
against the original "target" GMPE for a variety of magnitudes
and styles of fauling.
"""
low_gsim, mid_gsim, high_gsim = adj_gmpe_set
tot_std = [const.StdDev.TOTAL]
for imt in self.imts:
for mag in self.mags:
for rake in self.rakes:
rctx = RuptureContext()
rctx.mag = mag
rctx.rake = rake
rctx.hypo_depth = 10.
rctx.width = 0.0001
# Get "original" values
mean = original_gmpe.get_mean_and_stddevs(
self.sctx, rctx, self.dctx, imt, tot_std)[0]
mean = np.exp(mean)
# Get "low" adjustments (0.75 times the original)
low_mean = low_gsim.get_mean_and_stddevs(
self.sctx, rctx, self.dctx, imt, tot_std)[0]
np.testing.assert_array_almost_equal(
np.exp(low_mean) / mean, 0.75 * np.ones_like(low_mean))
# Get "middle" adjustments (1.25 times the original)
mid_mean = mid_gsim.get_mean_and_stddevs(
self.sctx, rctx, self.dctx, imt, tot_std)[0]
np.testing.assert_array_almost_equal(
np.exp(mid_mean) / mean, 1.25 * np.ones_like(mid_mean))
# Get "high" adjustments (1.5 times the original)
high_mean = high_gsim.get_mean_and_stddevs(
self.sctx, rctx, self.dctx, imt, tot_std)[0]
np.testing.assert_array_almost_equal(
np.exp(high_mean) / mean,
1.5 * np.ones_like(high_mean))
def build_gmpe_table(matrixMagsMin, matrixMagsMax, matrixMagsStep,
matrixDistsMin, matrixDistsMax, matrixDistsStep,
imt_filtering, limitIM, gsim_list, limit_max_mag,
limit_min_mag):
# Define the magnitude range of interest, 5.0 - 9.0 every 0.1
mags = np.arange(matrixMagsMin, matrixMagsMax, matrixMagsStep)
# Define the distance range of interest, 0.0 - 300.0 every 1 km
dists = np.arange(matrixDistsMin, matrixDistsMax, matrixDistsStep)
# Define the Vs30 range of interest, 180.0 - 1000.0 every 1 m/s
vs30s = np.arange(180.0, 181., 1.)
gm_table = np.zeros([len(dists), len(mags), len(vs30s)])
stddevs = [const.StdDev.TOTAL]
gsim_tables = []
for gsim in gsim_list:
for i, mag in enumerate(mags):
for j, vs30 in enumerate(vs30s):
# The RuptureContext object holds all of the
# rupture related attributes (e.g. mag, rake, ztor, hypo_depth)
rctx = RuptureContext()
rctx.mag = mag
rctx.rake = 0.0
rctx.hypo_depth = 10
# The DistancesContext object holds all of the distance
# calculations (e.g. rjb, rrup, rx, ry0)
# OQ GMPEs are vectorised by distance - so this needs
# to be an array
dctx = DistancesContext()
dctx.rjb = np.copy(dists)
dctx.rrup = np.copy(dists)
# dctx.rhypo = np.copy(dists)
# The SitesContext object holds all of the site
# attributes - also an array
sctx = SitesContext()
# The attributes of the site array must be of the
# same size as the distances
sctx.vs30 = vs30 * np.ones_like(dists)
# GMPE produces 2 outputs, the means (well their
# natural logarithm) and standard deviations
gm_table[:, i, j], gm_stddevs = gsim.get_mean_and_stddevs(
sctx, rctx, dctx, imt_filtering,
stddevs)
gm_table_exp = np.exp(gm_table)
gsim_tables.append(gm_table_exp)
if len(gsim_list) == 1:
gm_table_final = gsim_tables[0]
else:
gm_table_final = np.maximum(gsim_tables[0], gsim_tables[1])
# These "if" exclude all ruptures above and below the limit magnitude
if limit_max_mag < matrixMagsMax:
indexMag = int((limit_max_mag - matrixMagsMin) / matrixMagsStep)
list_mag_to_exclude = np.arange(indexMag+1, len(mags))
gm_table_final[:, list_mag_to_exclude, 0] = 0.001
if limit_min_mag > matrixMagsMin:
indexMinMag = int((limit_min_mag - matrixMagsMin) / matrixMagsStep)
list_min_mag_to_exclude = np.arange(0, indexMinMag)
gm_table_final[:, list_min_mag_to_exclude, 0] = 0.001
gm_mask = gm_table_final >= limitIM
GMPEmatrix = gm_mask[:, :, 0]
return GMPEmatrix
'usp000a1b0', 'usp000d6vk', 'usp000fg9t', 'usp000g9h6', 'us2000gge9',
'us1000etmq', 'us2000dwh6', 'nc30228270', 'nc72282711', 'ci14383980',
'ci14607652', 'usp0009eq0'
]
# Calculate mean Arias intensity and the standard deviations.
Trav_Ia_mean = []
interSD = []
intraSD = []
for event in eventids:
partial_df = df.loc[df['USGS_eventID'] == event]
sx.vs30 = np.array(partial_df['Vs30(m/s)'])
dx.rrup = np.array(partial_df['rrup'])
rx.mag = np.array(partial_df['magnitude'])[0]
rx.rake = np.array(partial_df['rake_angle'])[0]
Ia_mean, sd = trav2003.get_mean_and_stddevs(sx, rx, dx, imt, sd_types)
Trav_Ia_mean.append(np.exp(Ia_mean))
interSD.append(sd[0])
intraSD.append(sd[1])
# Flatten lists.
flat_Ia = [val for sublist in Trav_Ia_mean for val in sublist]
flat_interSD = [val for sublist in interSD for val in sublist]
flat_intraSD = [val for sublist in intraSD for val in sublist]
# Calculate residuals.
Ia_obs = np.array(df['Ia_arith(m/s)'])
logIa_obs = np.log(Ia_obs)
def signal_end(st, event_time, event_lon, event_lat, event_mag,
method=None, vmin=None, floor=None,
model=None, epsilon=2.0):
"""
Estimate end of signal by using a model of the 5-95% significant
duration, and adding this value to the "signal_split" time. This probably
only works well when the split is estimated with a p-wave picker since
the velocity method often ends up with split times that are well before
signal actually starts.
Args:
st (StationStream):
Stream of data.
event_time (UTCDateTime):
Event origin time.
event_mag (float):
Event magnitude.
event_lon (float):
Event longitude.
event_lat (float):
Event latitude.
method (str):
Method for estimating signal end time. Either 'velocity'
or 'model'.
vmin (float):
Velocity (km/s) for estimating end of signal. Only used if
method="velocity".
floor (float):
Minimum duration (sec) applied along with vmin.
model (str):
Short name of duration model to use. Must be defined in the
gmprocess/data/modules.yml file.
epsilon (float):
Number of standard deviations; if epsilon is 1.0, then the signal
window duration is the mean Ds + 1 standard deviation. Only used
for method="model".
Returns:
trace with stats dict updated to include a
stats['processing_parameters']['signal_end'] dictionary.
"""
# Load openquake stuff if method="model"
if method == "model":
mod_file = pkg_resources.resource_filename(
'gmprocess', os.path.join('data', 'modules.yml'))
with open(mod_file, 'r') as f:
mods = yaml.load(f)
# Import module
cname, mpath = mods['modules'][model]
dmodel = getattr(import_module(mpath), cname)()
# Set some "conservative" inputs (in that they will tend to give
# larger durations).
sctx = SitesContext()
sctx.vs30 = np.array([180.0])
sctx.z1pt0 = np.array([0.51])
rctx = RuptureContext()
rctx.mag = event_mag
rctx.rake = -90.0
dur_imt = imt.from_string('RSD595')
stddev_types = [const.StdDev.INTRA_EVENT]
for tr in st:
if not tr.hasParameter('signal_split'):
continue
if method == "velocity":
if vmin is None:
raise ValueError('Must specify vmin if method is "velocity".')
if floor is None:
raise ValueError('Must specify floor if method is "velocity".')
epi_dist = gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats['coordinates']['latitude'],
lon2=tr.stats['coordinates']['longitude'])[0] / 1000.0
end_time = event_time + max(floor, epi_dist / vmin)
elif method == "model":
if model is None:
raise ValueError('Must specify model if method is "model".')
epi_dist = gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats['coordinates']['latitude'],
lon2=tr.stats['coordinates']['longitude'])[0] / 1000.0
dctx = DistancesContext()
# Repi >= Rrup, so substitution here should be conservative
# (leading to larger durations).
dctx.rrup = np.array([epi_dist])
lnmu, lnstd = dmodel.get_mean_and_stddevs(
sctx, rctx, dctx, dur_imt, stddev_types)
duration = np.exp(lnmu + epsilon * lnstd[0])
# Get split time
split_time = tr.getParameter('signal_split')['split_time']
end_time = split_time + float(duration)
else:
raise ValueError('method must be either "velocity" or "model".')
# Update trace params
end_params = {
'end_time': end_time,
'method': method,
'vsplit': vmin,
'floor': floor,
'model': model,
'epsilon': epsilon
}
tr.setParameter('signal_end', end_params)
return st
def test_scr_rlme():
old_gmpe = set_gmpe('stable_continental_nshmp2014_rlme')
spec_file = pkg_resources.resource_filename(
'scenarios', os.path.join('data', 'configspec.conf'))
validator = get_custom_validator()
config = ConfigObj(os.path.join(os.path.expanduser('~'), 'scenarios.conf'),
configspec=spec_file)
tmp = pkg_resources.resource_filename(
'scenarios', os.path.join('..', 'data', 'gmpe_sets.conf'))
config.merge(ConfigObj(tmp, configspec=spec_file))
tmp = pkg_resources.resource_filename(
'scenarios', os.path.join('..', 'data', 'modules.conf'))
config.merge(ConfigObj(tmp, configspec=spec_file))
results = config.validate(validator)
if results != True:
config_error(config, results)
# MultiGMPE from config
config = config.dict()
gmpe = MultiGMPE.from_config(config)
# Input stuff
IMT = imt.SA(1.0)
rctx = RuptureContext()
dctx = DistancesContext()
sctx = SitesContext()
rctx.rake = 0.0
rctx.dip = 90.0
rctx.ztor = 0.0
rctx.mag = 8.0
rctx.width = 10.0
rctx.hypo_depth = 8.0
dctx.rjb = np.logspace(1, np.log10(800), 100)
dctx.rrup = dctx.rjb
dctx.rhypo = dctx.rjb
dctx.rx = dctx.rjb
dctx.ry0 = dctx.rjb
sctx.vs30 = np.ones_like(dctx.rjb) * 275.0
sctx.vs30measured = np.full_like(dctx.rjb, False, dtype='bool')
sctx = MultiGMPE.set_sites_depth_parameters(sctx, gmpe)
# Evaluate
conf_lmean, dummy = gmpe.get_mean_and_stddevs(sctx, rctx, dctx, IMT,
[const.StdDev.TOTAL])
target_lmean = np.array([
0.10556736, 0.0839267, 0.06189444, 0.03945984, 0.01661264, -0.006657,
-0.03035844, -0.05450058, -0.07909179, -0.10413995, -0.1296524,
-0.15563655, -0.1821091, -0.20909381, -0.23661405, -0.26469259,
-0.29335086, -0.32257956, -0.35232905, -0.38254639, -0.41317807,
-0.44417017, -0.47549552, -0.5071888, -0.53929293, -0.57185042,
-0.60490345, -0.63848027, -0.67255251, -0.70707712, -0.74201096,
-0.77731091, -0.81293906, -0.84889737, -0.88520644, -0.92188724,
-0.95899471, -0.99699613, -1.03583184, -1.07530664, -1.11531737,
-1.15576129, -1.19653696, -1.23757689, -1.2772327, -1.2915098,
-1.30576498, -1.32001713, -1.33429606, -1.3486727, -1.36322545,
-1.37803346, -1.39317668, -1.40677752, -1.42081409, -1.43538898,
-1.45056417, -1.46640223, -1.48327111, -1.50656497, -1.53368548,
-1.56645985, -1.59991327, -1.63399401, -1.66867278, -1.7039438,
-1.73980246, -1.77624473, -1.81326727, -1.85087166, -1.889066,
-1.92784814, -1.96721442, -2.0071855, -2.04779304, -2.08909259,
-2.13114448, -2.17401045, -2.21775376, -2.26243406, -2.30808979,
-2.35475487, -2.40246494, -2.4512575, -2.50117075, -2.55223495,
-2.60447754, -2.65792811, -2.71261851, -2.61732716, -2.67007323,
-2.72399057, -2.77918054, -2.83574666, -2.89379416, -2.95340501,
-3.01462691, -3.07750731, -3.14209631, -3.20844679
])
np.testing.assert_allclose(conf_lmean, target_lmean, atol=1e-6)
# Redo for 3 sec so some GMPEs are filtered out
IMT = imt.SA(3.0)
gmpe = MultiGMPE.from_config(config, filter_imt=IMT)
conf_lmean, dummy = gmpe.get_mean_and_stddevs(sctx, rctx, dctx, IMT,
[const.StdDev.TOTAL])
target_lmean = np.array([
-1.26636973, -1.289514, -1.31300386, -1.33683936, -1.36102084,
-1.38554902, -1.41042497, -1.43565015, -1.46122642, -1.48715602,
-1.51344154, -1.54008586, -1.56709215, -1.59446375, -1.62220409,
-1.65031664, -1.6788048, -1.70767178, -1.7369205, -1.76655351,
-1.79657287, -1.82698005, -1.85777587, -1.88896039, -1.92053288,
-1.95249175, -1.98483453, -2.01755788, -2.05065755, -2.08412844,
-2.11796463, -2.15215943, -2.18670547, -2.22159473, -2.25681869,
-2.29236835, -2.32823441, -2.36453464, -2.40140834, -2.43883442,
-2.47679132, -2.51525752, -2.55421156, -2.59363211, -2.63112832,
-2.63336521, -2.63582817, -2.6385319, -2.64147962, -2.64466761,
-2.64809268, -2.65175214, -2.6556438, -2.65976592, -2.66411721,
-2.66869673, -2.67350386, -2.67853821, -2.68413311, -2.69604497,
-2.7124745, -2.73590549, -2.75964098, -2.78367044, -2.80798539,
-2.8325853, -2.85746998, -2.88263948, -2.90809408, -2.93383429,
-2.95986073, -2.98617306, -3.01275705, -3.03961495, -3.06675608,
-3.09419043, -3.12192861, -3.14998191, -3.17836228, -3.20708239,
-3.23615561, -3.26559604, -3.29541858, -3.32563888, -3.35627343,
-3.38733956, -3.41885548, -3.4508403, -3.48331409, -3.56476842,
-3.59987076, -3.63573296, -3.67238872, -3.70987332, -3.74822369,
-3.78747847, -3.82767809, -3.86886488, -3.91108308, -3.95437899
])
np.testing.assert_allclose(conf_lmean, target_lmean, atol=1e-6)
# Clean up
set_gmpe(old_gmpe)
def get_extent(rupture=None, config=None):
"""
Method to compute map extent from rupture. There are numerous methods for
getting the extent:
- It can be specified directly in the config file,
- it can be hard coded for specific magnitude ranges in the config
file, or
- it can be based on the MultiGMPE for the event.
All methods except for the first requires a rupture object.
If no config is provided then a rupture is required and the extent is based
on a generic set of active/stable.
Args:
rupture (Rupture): A ShakeMap Rupture instance.
config (ConfigObj): ShakeMap config object.
Returns:
tuple: lonmin, lonmax, latmin, latmax rounded to the nearest
arc-minute..
"""
# -------------------------------------------------------------------------
# Check to see what parameters are specified in the extent config
# -------------------------------------------------------------------------
spans = {}
bounds = []
if config is not None:
if 'extent' in config:
if 'magnitude_spans' in config['extent']:
if len(config['extent']['magnitude_spans']):
if isinstance(config['extent']['magnitude_spans'], dict):
spans = config['extent']['magnitude_spans']
if 'bounds' in config['extent']:
if 'extent' in config['extent']['bounds']:
if config['extent']['bounds']['extent'][0] != -999.0:
bounds = config['extent']['bounds']['extent']
# -------------------------------------------------------------------------
# Simplest option: extent was specified in the config, use that and exit.
# -------------------------------------------------------------------------
if len(bounds):
xmin, ymin, xmax, ymax = bounds
return (xmin, xmax, ymin, ymax)
if not rupture or not isinstance(rupture, Rupture):
raise TypeError('get_extent() requires a rupture object if the extent '
'is not specified in the config object.')
# Find the central point
origin = rupture.getOrigin()
if isinstance(rupture, (QuadRupture, EdgeRupture)):
# For an extended rupture, it is the midpoint between the extent of the
# verticies
lats = rupture.lats
lons = rupture.lons
# Remove nans
lons = lons[~np.isnan(lons)]
lats = lats[~np.isnan(lats)]
clat = 0.5 * (np.nanmax(lats) + np.nanmin(lats))
clon = 0.5 * (np.nanmax(lons) + np.nanmin(lons))
else:
# For a point source, it is just the epicenter
clat = origin.lat
clon = origin.lon
mag = origin.mag
# -------------------------------------------------------------------------
# Second simplest option: spans are hardcoded based on magnitude
# -------------------------------------------------------------------------
if len(spans):
xmin = None
xmax = None
ymin = None
ymax = None
for spankey, span in spans.items():
if mag > span[0] and mag <= span[1]:
ymin = clat - span[2] / 2
ymax = clat + span[2] / 2
xmin = clon - span[3] / 2
xmax = clon + span[3] / 2
break
if xmin is not None:
return (xmin, xmax, ymin, ymax)
# -------------------------------------------------------------------------
# Use MultiGMPE to get spans
# -------------------------------------------------------------------------
if config is not None:
gmpe = MultiGMPE.from_config(config)
gmice = get_object_from_config('gmice', 'modeling', config)
else:
# Put in some default values for conf
config = {
'extent': {
'mmi': {
'threshold': 4.5,
'mindist': 100,
'maxdist': 1000
}
}
}
# Generic GMPEs choices based only on active vs stable
# as defaults...
stable = is_stable(origin.lon, origin.lat)
if not stable:
ASK14 = AbrahamsonEtAl2014()
CB14 = CampbellBozorgnia2014()
CY14 = ChiouYoungs2014()
gmpes = [ASK14, CB14, CY14]
site_gmpes = None
weights = [1 / 3.0, 1 / 3.0, 1 / 3.0]
gmice = WGRW12()
else:
Fea96 = FrankelEtAl1996MwNSHMP2008()
Tea97 = ToroEtAl1997MwNSHMP2008()
Sea02 = SilvaEtAl2002MwNSHMP2008()
C03 = Campbell2003MwNSHMP2008()
TP05 = TavakoliPezeshk2005MwNSHMP2008()
AB06p = AtkinsonBoore2006Modified2011()
Pea11 = PezeshkEtAl2011()
Atk08p = Atkinson2008prime()
Sea01 = SomervilleEtAl2001NSHMP2008()
gmpes = [
Fea96, Tea97, Sea02, C03, TP05, AB06p, Pea11, Atk08p, Sea01
]
site_gmpes = [AB06p]
weights = [0.16, 0.0, 0.0, 0.17, 0.17, 0.3, 0.2, 0.0, 0.0]
gmice = AK07()
gmpe = MultiGMPE.from_list(gmpes,
weights,
default_gmpes_for_site=site_gmpes)
min_mmi = config['extent']['mmi']['threshold']
default_imt = imt.SA(1.0)
sd_types = [const.StdDev.TOTAL]
# Distance context
dx = DistancesContext()
# This imposes minimum/ maximum distances of:
# 80 and 800 km; could make this configurable
d_min = config['extent']['mmi']['mindist']
d_max = config['extent']['mmi']['maxdist']
dx.rjb = np.logspace(np.log10(d_min), np.log10(d_max), 2000)
# Details don't matter for this; assuming vertical surface rupturing fault
# with epicenter at the surface.
dx.rrup = dx.rjb
dx.rhypo = dx.rjb
dx.repi = dx.rjb
dx.rx = np.zeros_like(dx.rjb)
dx.ry0 = np.zeros_like(dx.rjb)
dx.rvolc = np.zeros_like(dx.rjb)
# Sites context
sx = SitesContext()
# Set to soft soil conditions
sx.vs30 = np.full_like(dx.rjb, 180)
sx = MultiGMPE.set_sites_depth_parameters(sx, gmpe)
sx.vs30measured = np.full_like(sx.vs30, False, dtype=bool)
sx = Sites._addDepthParameters(sx)
sx.backarc = np.full_like(sx.vs30, False, dtype=bool)
# Rupture context
rx = RuptureContext()
rx.mag = origin.mag
rx.rake = 0.0
# From WC94...
rx.width = 10**(-0.76 + 0.27 * rx.mag)
rx.dip = 90.0
rx.ztor = origin.depth
rx.hypo_depth = origin.depth
gmpe_imt_mean, _ = gmpe.get_mean_and_stddevs(sx, rx, dx, default_imt,
sd_types)
# Convert to MMI
gmpe_to_mmi, _ = gmice.getMIfromGM(gmpe_imt_mean, default_imt)
# Minimum distance that exceeds threshold MMI?
dists_exceed_mmi = dx.rjb[gmpe_to_mmi > min_mmi]
if len(dists_exceed_mmi):
mindist_km = np.max(dists_exceed_mmi)
else:
mindist_km = d_min
# Get a projection
proj = OrthographicProjection(clon - 4, clon + 4, clat + 4, clat - 4)
if isinstance(rupture, (QuadRupture, EdgeRupture)):
ruptx, rupty = proj(lons, lats)
else:
ruptx, rupty = proj(clon, clat)
xmin = np.nanmin(ruptx) - mindist_km
ymin = np.nanmin(rupty) - mindist_km
xmax = np.nanmax(ruptx) + mindist_km
ymax = np.nanmax(rupty) + mindist_km
# Put a limit on range of aspect ratio
dx = xmax - xmin
dy = ymax - ymin
ar = dy / dx
if ar > 1.2:
# Inflate x
dx_target = dy / 1.2
ddx = dx_target - dx
xmax = xmax + ddx / 2
xmin = xmin - ddx / 2
if ar < 0.83:
# Inflate y
dy_target = dx * 0.83
ddy = dy_target - dy
ymax = ymax + ddy / 2
ymin = ymin - ddy / 2
lonmin, latmin = proj(np.array([xmin]), np.array([ymin]), reverse=True)
lonmax, latmax = proj(np.array([xmax]), np.array([ymax]), reverse=True)
#
# Round coordinates to the nearest minute -- that should make the
# output grid register with common grid resolutions (60c, 30c,
# 15c, 7.5c)
#
logging.debug("Extent: %f, %f, %f, %f" % (lonmin, lonmax, latmin, latmax))
return _round_coord(lonmin[0]), _round_coord(lonmax[0]), \
_round_coord(latmin[0]), _round_coord(latmax[0])
def _get_extent_from_multigmpe(rupture, config=None):
"""
Use MultiGMPE to determine extent
"""
(clon, clat) = _rupture_center(rupture)
origin = rupture.getOrigin()
if config is not None:
gmpe = MultiGMPE.from_config(config)
gmice = get_object_from_config('gmice', 'modeling', config)
if imt.SA in gmice.DEFINED_FOR_INTENSITY_MEASURE_TYPES:
default_imt = imt.SA(1.0)
elif imt.PGV in gmice.DEFINED_FOR_INTENSITY_MEASURE_TYPES:
default_imt = imt.PGV()
else:
default_imt = imt.PGA()
else:
# Put in some default values for conf
config = {
'extent': {
'mmi': {
'threshold': 4.5,
'mindist': 100,
'maxdist': 1000
}
}
}
# Generic GMPEs choices based only on active vs stable
# as defaults...
stable = is_stable(origin.lon, origin.lat)
if not stable:
ASK14 = AbrahamsonEtAl2014()
CB14 = CampbellBozorgnia2014()
CY14 = ChiouYoungs2014()
gmpes = [ASK14, CB14, CY14]
site_gmpes = None
weights = [1/3.0, 1/3.0, 1/3.0]
gmice = WGRW12()
else:
Fea96 = FrankelEtAl1996MwNSHMP2008()
Tea97 = ToroEtAl1997MwNSHMP2008()
Sea02 = SilvaEtAl2002MwNSHMP2008()
C03 = Campbell2003MwNSHMP2008()
TP05 = TavakoliPezeshk2005MwNSHMP2008()
AB06p = AtkinsonBoore2006Modified2011()
Pea11 = PezeshkEtAl2011()
Atk08p = Atkinson2008prime()
Sea01 = SomervilleEtAl2001NSHMP2008()
gmpes = [Fea96, Tea97, Sea02, C03,
TP05, AB06p, Pea11, Atk08p, Sea01]
site_gmpes = [AB06p]
weights = [0.16, 0.0, 0.0, 0.17, 0.17, 0.3, 0.2, 0.0, 0.0]
gmice = AK07()
gmpe = MultiGMPE.from_list(
gmpes, weights, default_gmpes_for_site=site_gmpes)
default_imt = imt.SA(1.0)
min_mmi = config['extent']['mmi']['threshold']
sd_types = [const.StdDev.TOTAL]
# Distance context
dx = DistancesContext()
# This imposes minimum/ maximum distances of:
# 80 and 800 km; could make this configurable
d_min = config['extent']['mmi']['mindist']
d_max = config['extent']['mmi']['maxdist']
dx.rjb = np.logspace(np.log10(d_min), np.log10(d_max), 2000)
# Details don't matter for this; assuming vertical surface rupturing fault
# with epicenter at the surface.
dx.rrup = dx.rjb
dx.rhypo = dx.rjb
dx.repi = dx.rjb
dx.rx = np.zeros_like(dx.rjb)
dx.ry0 = np.zeros_like(dx.rjb)
dx.rvolc = np.zeros_like(dx.rjb)
# Sites context
sx = SitesContext()
# Set to soft soil conditions
sx.vs30 = np.full_like(dx.rjb, 180)
sx = MultiGMPE.set_sites_depth_parameters(sx, gmpe)
sx.vs30measured = np.full_like(sx.vs30, False, dtype=bool)
sx = Sites._addDepthParameters(sx)
sx.backarc = np.full_like(sx.vs30, False, dtype=bool)
# Rupture context
rx = RuptureContext()
rx.mag = origin.mag
rx.rake = 0.0
# From WC94...
rx.width = 10**(-0.76 + 0.27*rx.mag)
rx.dip = 90.0
rx.ztor = origin.depth
rx.hypo_depth = origin.depth
gmpe_imt_mean, _ = gmpe.get_mean_and_stddevs(
sx, rx, dx, default_imt, sd_types)
# Convert to MMI
gmpe_to_mmi, _ = gmice.getMIfromGM(gmpe_imt_mean, default_imt)
# Minimum distance that exceeds threshold MMI?
dists_exceed_mmi = dx.rjb[gmpe_to_mmi > min_mmi]
if len(dists_exceed_mmi):
mindist_km = np.max(dists_exceed_mmi)
else:
mindist_km = d_min
# Get a projection
proj = OrthographicProjection(clon - 4, clon + 4, clat + 4, clat - 4)
if isinstance(rupture, (QuadRupture, EdgeRupture)):
ruptx, rupty = proj(
rupture.lons[~np.isnan(rupture.lons)],
rupture.lats[~np.isnan(rupture.lats)]
)
else:
ruptx, rupty = proj(clon, clat)
xmin = np.nanmin(ruptx) - mindist_km
ymin = np.nanmin(rupty) - mindist_km
xmax = np.nanmax(ruptx) + mindist_km
ymax = np.nanmax(rupty) + mindist_km
# Put a limit on range of aspect ratio
dx = xmax - xmin
dy = ymax - ymin
ar = dy / dx
if ar > 1.2:
# Inflate x
dx_target = dy / 1.2
ddx = dx_target - dx
xmax = xmax + ddx / 2
xmin = xmin - ddx / 2
if ar < 0.83:
# Inflate y
dy_target = dx * 0.83
ddy = dy_target - dy
ymax = ymax + ddy / 2
ymin = ymin - ddy / 2
lonmin, latmin = proj(np.array([xmin]), np.array([ymin]), reverse=True)
lonmax, latmax = proj(np.array([xmax]), np.array([ymax]), reverse=True)
#
# Round coordinates to the nearest minute -- that should make the
# output grid register with common grid resolutions (60c, 30c,
# 15c, 7.5c)
#
logging.debug("Extent: %f, %f, %f, %f" %
(lonmin, lonmax, latmin, latmax))
return _round_coord(lonmin[0]), _round_coord(lonmax[0]), \
_round_coord(latmin[0]), _round_coord(latmax[0])
def signal_end(st,
event_time,
event_lon,
event_lat,
event_mag,
method=None,
vmin=None,
floor=None,
model=None,
epsilon=2.0):
"""
Estimate end of signal by using a model of the 5-95% significant
duration, and adding this value to the "signal_split" time. This probably
only works well when the split is estimated with a p-wave picker since
the velocity method often ends up with split times that are well before
signal actually starts.
Args:
st (StationStream):
Stream of data.
event_time (UTCDateTime):
Event origin time.
event_mag (float):
Event magnitude.
event_lon (float):
Event longitude.
event_lat (float):
Event latitude.
method (str):
Method for estimating signal end time. Either 'velocity'
or 'model'.
vmin (float):
Velocity (km/s) for estimating end of signal. Only used if
method="velocity".
floor (float):
Minimum duration (sec) applied along with vmin.
model (str):
Short name of duration model to use. Must be defined in the
gmprocess/data/modules.yml file.
epsilon (float):
Number of standard deviations; if epsilon is 1.0, then the signal
window duration is the mean Ds + 1 standard deviation. Only used
for method="model".
Returns:
trace with stats dict updated to include a
stats['processing_parameters']['signal_end'] dictionary.
"""
# Load openquake stuff if method="model"
if method == "model":
mod_file = pkg_resources.resource_filename(
'gmprocess', os.path.join('data', 'modules.yml'))
with open(mod_file, 'r') as f:
mods = yaml.load(f, Loader=yaml.FullLoader)
# Import module
cname, mpath = mods['modules'][model]
dmodel = getattr(import_module(mpath), cname)()
# Set some "conservative" inputs (in that they will tend to give
# larger durations).
sctx = SitesContext()
sctx.vs30 = np.array([180.0])
sctx.z1pt0 = np.array([0.51])
rctx = RuptureContext()
rctx.mag = event_mag
rctx.rake = -90.0
dur_imt = imt.from_string('RSD595')
stddev_types = [const.StdDev.INTRA_EVENT]
for tr in st:
if not tr.hasParameter('signal_split'):
continue
if method == "velocity":
if vmin is None:
raise ValueError('Must specify vmin if method is "velocity".')
if floor is None:
raise ValueError('Must specify floor if method is "velocity".')
epi_dist = gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats['coordinates']['latitude'],
lon2=tr.stats['coordinates']['longitude'])[0] / 1000.0
end_time = event_time + max(floor, epi_dist / vmin)
elif method == "model":
if model is None:
raise ValueError('Must specify model if method is "model".')
epi_dist = gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats['coordinates']['latitude'],
lon2=tr.stats['coordinates']['longitude'])[0] / 1000.0
dctx = DistancesContext()
# Repi >= Rrup, so substitution here should be conservative
# (leading to larger durations).
dctx.rrup = np.array([epi_dist])
lnmu, lnstd = dmodel.get_mean_and_stddevs(sctx, rctx, dctx,
dur_imt, stddev_types)
duration = np.exp(lnmu + epsilon * lnstd[0])
# Get split time
split_time = tr.getParameter('signal_split')['split_time']
end_time = split_time + float(duration)
else:
raise ValueError('method must be either "velocity" or "model".')
# Update trace params
end_params = {
'end_time': end_time,
'method': method,
'vsplit': vmin,
'floor': floor,
'model': model,
'epsilon': epsilon
}
tr.setParameter('signal_end', end_params)
return st
def trim_multiple_events(
st,
origin,
catalog,
travel_time_df,
pga_factor,
pct_window_reject,
gmpe,
site_parameters,
rupture_parameters,
):
"""
Uses a catalog (list of ScalarEvents) to handle cases where a trace might
contain signals from multiple events. The catalog should contain events
down to a low enough magnitude in relation to the events of interest.
Overall, the algorithm is as follows:
1) For each earthquake in the catalog, get the P-wave travel time
and estimated PGA at this station.
2) Compute the PGA (of the as-recorded horizontal channels).
3) Select the P-wave arrival times across all events for this record
that are (a) within the signal window, and (b) the predicted PGA is
greater than pga_factor times the PGA from step #1.
4) If any P-wave arrival times match the above criteria, then if any of
the arrival times fall within in the first pct_window_reject*100%
of the signal window, then reject the record. Otherwise, trim the
record such that the end time does not include any of the arrivals
selected in step #3.
Args:
st (StationStream):
Stream of data.
origin (ScalarEvent):
ScalarEvent object associated with the StationStream.
catalog (list):
List of ScalarEvent objects.
travel_time_df (DataFrame):
A pandas DataFrame that contains the travel time information
(obtained from
gmprocess.waveform_processing.phase.create_travel_time_dataframe).
The columns in the DataFrame are the station ids and the indices
are the earthquake ids.
pga_factor (float):
A decimal factor used to determine whether the predicted PGA
from an event arrival is significant enough that it should be
considered for removal.
pct_window_reject (float):
A decimal from 0.0 to 1.0 used to determine if an arrival should
be trimmed from the record, or if the entire record should be
rejected. If the arrival falls within the first
pct_window_reject * 100% of the signal window, then the entire
record will be rejected. Otherwise, the record will be trimmed
appropriately.
gmpe (str):
Short name of the GMPE to use. Must be defined in the modules file.
site_parameters (dict):
Dictionary of site parameters to input to the GMPE.
rupture_parameters:
Dictionary of rupture parameters to input to the GMPE.
Returns:
StationStream: Processed stream.
"""
if not st.passed:
return st
# Check that we know the signal split for each trace in the stream
for tr in st:
if not tr.hasParameter("signal_split"):
return st
signal_window_starttime = st[0].getParameter("signal_split")["split_time"]
arrivals = travel_time_df[st[0].stats.network + "." + st[0].stats.station]
arrivals = arrivals.sort_values()
# Filter by any arrival times that appear in the signal window
arrivals = arrivals[(arrivals > signal_window_starttime)
& (arrivals < st[0].stats.endtime)]
# Make sure we remove the arrival that corresponds to the event of interest
if origin.id in arrivals.index:
arrivals.drop(index=origin.id, inplace=True)
if arrivals.empty:
return st
# Calculate the recorded PGA for this record
stasum = StationSummary.from_stream(st, ["ROTD(50.0)"], ["PGA"])
recorded_pga = stasum.get_pgm("PGA", "ROTD(50.0)")
# Load the GMPE model
gmpe = load_model(gmpe)
# Generic context
rctx = RuptureContext()
# Make sure that site parameter values are converted to numpy arrays
site_parameters_copy = site_parameters.copy()
for k, v in site_parameters_copy.items():
site_parameters_copy[k] = np.array([site_parameters_copy[k]])
rctx.__dict__.update(site_parameters_copy)
# Filter by arrivals that have significant expected PGA using GMPE
is_significant = []
for eqid, arrival_time in arrivals.items():
event = next(event for event in catalog if event.id == eqid)
# Set rupture parameters
rctx.__dict__.update(rupture_parameters)
rctx.mag = event.magnitude
# TODO: distances should be calculated when we refactor to be
# able to import distance calculations
rctx.repi = np.array([
gps2dist_azimuth(
st[0].stats.coordinates.latitude,
st[0].stats.coordinates.longitude,
event.latitude,
event.longitude,
)[0] / 1000
])
rctx.rjb = rctx.repi
rctx.rhypo = np.sqrt(rctx.repi**2 + event.depth_km**2)
rctx.rrup = rctx.rhypo
rctx.sids = np.array(range(np.size(rctx.rrup)))
pga, sd = gmpe.get_mean_and_stddevs(rctx, rctx, rctx, imt.PGA(), [])
# Convert from ln(g) to %g
predicted_pga = 100 * np.exp(pga[0])
if predicted_pga > (pga_factor * recorded_pga):
is_significant.append(True)
else:
is_significant.append(False)
significant_arrivals = arrivals[is_significant]
if significant_arrivals.empty:
return st
# Check if any of the significant arrivals occur within the
signal_length = st[0].stats.endtime - signal_window_starttime
cutoff_time = signal_window_starttime + pct_window_reject * (signal_length)
if (significant_arrivals < cutoff_time).any():
for tr in st:
tr.fail("A significant arrival from another event occurs within "
"the first %s percent of the signal window" %
(100 * pct_window_reject))
# Otherwise, trim the stream at the first significant arrival
else:
for tr in st:
signal_end = tr.getParameter("signal_end")
signal_end["end_time"] = significant_arrivals[0]
signal_end["method"] = "Trimming before right another event"
tr.setParameter("signal_end", signal_end)
cut(st)
return st
def signal_end(
st,
event_time,
event_lon,
event_lat,
event_mag,
method=None,
vmin=None,
floor=None,
model=None,
epsilon=2.0,
):
"""
Estimate end of signal by using a model of the 5-95% significant
duration, and adding this value to the "signal_split" time. This probably
only works well when the split is estimated with a p-wave picker since
the velocity method often ends up with split times that are well before
signal actually starts.
Args:
st (StationStream):
Stream of data.
event_time (UTCDateTime):
Event origin time.
event_mag (float):
Event magnitude.
event_lon (float):
Event longitude.
event_lat (float):
Event latitude.
method (str):
Method for estimating signal end time. Either 'velocity'
or 'model'.
vmin (float):
Velocity (km/s) for estimating end of signal. Only used if
method="velocity".
floor (float):
Minimum duration (sec) applied along with vmin.
model (str):
Short name of duration model to use. Must be defined in the
gmprocess/data/modules.yml file.
epsilon (float):
Number of standard deviations; if epsilon is 1.0, then the signal
window duration is the mean Ds + 1 standard deviation. Only used
for method="model".
Returns:
trace with stats dict updated to include a
stats['processing_parameters']['signal_end'] dictionary.
"""
# Load openquake stuff if method="model"
if method == "model":
dmodel = load_model(model)
# Set some "conservative" inputs (in that they will tend to give
# larger durations).
rctx = RuptureContext()
rctx.mag = event_mag
rctx.rake = -90.0
rctx.vs30 = np.array([180.0])
rctx.z1pt0 = np.array([0.51])
dur_imt = imt.from_string("RSD595")
stddev_types = [const.StdDev.TOTAL]
for tr in st:
if not tr.hasParameter("signal_split"):
continue
if method == "velocity":
if vmin is None:
raise ValueError('Must specify vmin if method is "velocity".')
if floor is None:
raise ValueError('Must specify floor if method is "velocity".')
epi_dist = (gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats["coordinates"]["latitude"],
lon2=tr.stats["coordinates"]["longitude"],
)[0] / 1000.0)
end_time = event_time + max(floor, epi_dist / vmin)
elif method == "model":
if model is None:
raise ValueError('Must specify model if method is "model".')
epi_dist = (gps2dist_azimuth(
lat1=event_lat,
lon1=event_lon,
lat2=tr.stats["coordinates"]["latitude"],
lon2=tr.stats["coordinates"]["longitude"],
)[0] / 1000.0)
# Repi >= Rrup, so substitution here should be conservative
# (leading to larger durations).
rctx.rrup = np.array([epi_dist])
rctx.sids = np.array(range(np.size(rctx.rrup)))
lnmu, lnstd = dmodel.get_mean_and_stddevs(rctx, rctx, rctx,
dur_imt, stddev_types)
duration = np.exp(lnmu + epsilon * lnstd[0])
# Get split time
split_time = tr.getParameter("signal_split")["split_time"]
end_time = split_time + float(duration)
else:
raise ValueError('method must be either "velocity" or "model".')
# Update trace params
end_params = {
"end_time": end_time,
"method": method,
"vsplit": vmin,
"floor": floor,
"model": model,
"epsilon": epsilon,
}
tr.setParameter("signal_end", end_params)
return st
## Make the rupture and distance contexts:
i_rctx = RuptureContext()
i_dctx = DistancesContext()
i_sctx = SitesContext()
# Zhao & Travasarou wants rrup, but dont' have that - so set to rhypo;
# BSSA14 wants rjb but don't have it, so set to repi:
i_dctx.rrup = rhypo[recording_i]
i_dctx.rjb = repi[recording_i]
## Site:
i_sctx.vs30 = np.array([vs30_data[recording_i]])
## Rupture - USE THE PGD MAGNTIUDE!
i_rctx.rake = rake[recording_i]
i_rctx.mag = mw_pgd[recording_i]
i_rctx.hypo_depth = hypodepth[recording_i]
## Get thje Zhao predictions (in g), Travasarou (unitless), and BSSA (cm/s)
i_median_zhao2006, i_sd_zhao2006 = zhao2006.get_mean_and_stddevs(
i_sctx, i_rctx, i_dctx, imt_pga, [const.StdDev.TOTAL])
i_median_travasarou, i_sd_travasarou = travasarou.get_mean_and_stddevs(
i_sctx, i_rctx, i_dctx, imt_arias, [const.StdDev.TOTAL])
i_median_bssa14, i_sd_bssa14 = bssa14.get_mean_and_stddevs(
i_sctx, i_rctx, i_dctx, imt_pgv, [const.StdDev.TOTAL])
## Convert BSSA from cm/s to m/s, and keep in linear space:
i_median_bssa14 = np.exp(i_median_bssa14) * 1e-2
## Convert Zhao from g to m/s/s. and keep in linear space:
i_median_zhao2006 = np.exp(i_median_zhao2006) * g
## This all works..... ## ASK14 = AbrahamsonEtAl2014() IMT = imt.PGA() rctx = RuptureContext() dctx = DistancesContext() sctx = SitesContext() sctx_rock = SitesContext() rctx.rake = 0.0 rctx.dip = 90.0 rctx.ztor = 7.13 rctx.mag = 3.0 #rctx.mag = np.linspace(0.1,5.) rctx.width = 10.0 rctx.hypo_depth = 8.0 #dctx.rrup = np.logspace(1,np.log10(200),100) dctx.rrup = np.logspace(np.log10(10),np.log10(10.0),1) # Assuming average ztor, get rjb: dctx.rjb = np.sqrt(dctx.rrup**2 - rctx.ztor**2) dctx.rhypo = dctx.rrup dctx.rx = dctx.rjb dctx.ry0 = dctx.rx sctx.vs30 = np.ones_like(dctx.rrup) * 760.0
