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_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)
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 = 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(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)
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)
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 = 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(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)
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_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 getSitesContext(self):
"""
:returns:
SitesContext object.
"""
sctx = SitesContext()
sctx.vs30 = self._Vs30.getData().copy()
sctx.z1pt0 = self._Z1Pt0
sctx.z2pt5 = self._Z2Pt5
sctx.backarc = self._backarc # zoneconfig might have this info
if self._vs30measured_grid is None: # If we don't know, then use false
sctx.vs30measured = np.zeros_like(sctx.vs30, dtype=bool)
else:
sctx.vs30measured = self._vs30measured_grid
sctx.lons = self._lons
sctx.lats = self._lats
return sctx
def getSitesContext(self):
"""
:returns:
SitesContext object.
"""
sctx = SitesContext()
sctx.vs30 = self._Vs30.getData().copy()
sctx.z1pt0 = self._Z1Pt0
sctx.z2pt5 = self._Z2Pt5
sctx.backarc = self._backarc # zoneconfig might have this info
if self._vs30measured_grid is None: # If we don't know, then use false
sctx.vs30measured = np.zeros_like(
sctx.vs30, dtype=bool)
else:
sctx.vs30measured = self._vs30measured_grid
sctx.lons = self._lons
sctx.lats = self._lats
return sctx
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 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 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_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 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 sampleFromSites(self, lats, lons, vs30measured_grid=None):
"""
Create a SitesContext object by sampling the current Sites object.
:param lats:
Sequence of latitudes.
:param lons:
Sequence of longitudes.
:param vs30measured_grid:
Sequence of booleans of the same shape as lats/lons indicating
whether the vs30 values are measured or inferred.
:returns:
SitesContext object where data are sampled from the current Sites
object.
:raises ShakeMapException:
When lat/lon input sequences do not share dimensionality.
"""
lats = np.array(lats)
lons = np.array(lons)
latshape = lats.shape
lonshape = lons.shape
if latshape != lonshape:
msg = 'Input lat/lon arrays must have the same dimensions'
raise ShakeMapException(msg)
site = SitesContext()
# use default vs30 if outside grid
site.vs30 = self._Vs30.getValue(lats, lons, default=self._defaultVs30)
site.lats = lats
site.lons = lons
site.z1pt0 = _calculate_z1p0(site.vs30)
site.z2pt5 = _calculate_z2p5(site.z1pt0)
if vs30measured_grid is None: # If we don't know, then use false
site.vs30measured = np.zeros_like(lons, dtype=bool)
else:
site.vs30measured = vs30measured_grid
site.backarc = self._backarc
return site
def sampleFromSites(self, lats, lons, vs30measured_grid=None):
"""
Create a SitesContext object by sampling the current Sites object.
:param lats:
Sequence of latitudes.
:param lons:
Sequence of longitudes.
:param vs30measured_grid:
Sequence of booleans of the same shape as lats/lons indicating
whether the vs30 values are measured or inferred.
:returns:
SitesContext object where data are sampled from the current Sites
object.
:raises ShakeMapException:
When lat/lon input sequences do not share dimensionality.
"""
lats = np.array(lats)
lons = np.array(lons)
latshape = lats.shape
lonshape = lons.shape
if latshape != lonshape:
msg = 'Input lat/lon arrays must have the same dimensions'
raise ShakeMapException(msg)
site = SitesContext()
# use default vs30 if outside grid
site.vs30 = self._Vs30.getValue(lats, lons, default=self._defaultVs30)
site.lats = lats
site.lons = lons
site.z1pt0 = _calculate_z1p0(site.vs30)
site.z2pt5 = _calculate_z2p5(site.z1pt0)
if vs30measured_grid is None: # If we don't know, then use false
site.vs30measured = np.zeros_like(lons, dtype=bool)
else:
site.vs30measured = vs30measured_grid
site.backarc = self._backarc
return site
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 getSitesContext(self, lldict=None, rock_vs30=None):
"""
Create a SitesContext object by sampling the current Sites object.
Args:
lldict: Either
- None, in which case the SitesContext for the complete Sites
grid is returned, or
- A location dictionary (elements are 'lats' and 'lons' and
each is a numpy array). Each element must have the same
shape. In this case the SitesContext for these locaitons is
returned.
rock_vs30: Either
- None, in which case the SitesContext will reflect the Vs30
grid in the Sites instance, or
- A float for the rock Vs30 value, in which case the
SitesContext will be constructed for this constant Vs30
value.
Returns:
SitesContext object.
Raises:
ShakeLibException: When lat/lon input sequences do not share
dimensionality.
""" # noqa
sctx = SitesContext()
if lldict is not None:
lats = lldict['lats']
lons = lldict['lons']
latshape = lats.shape
lonshape = lons.shape
if latshape != lonshape:
msg = 'Input lat/lon arrays must have the same dimensions'
raise ShakeLibException(msg)
if rock_vs30 is not None:
tmp = self._Vs30.getValue(
lats, lons, default=self._defaultVs30)
sctx.vs30 = np.ones_like(tmp) * rock_vs30
else:
sctx.vs30 = self._Vs30.getValue(
lats, lons, default=self._defaultVs30)
sctx.lats = lats
sctx.lons = lons
else:
sctx.lats = self._lats.copy()
sctx.lons = self._lons.copy()
if rock_vs30 is not None:
sctx.vs30 = np.ones_like(self._Vs30.getData()) * rock_vs30
else:
sctx.vs30 = self._Vs30.getData().copy()
sctx = Sites._addDepthParameters(sctx)
# For ShakeMap purposes, vs30 measured is always Fales
sctx.vs30measured = np.zeros_like(sctx.vs30, dtype=bool)
# Backarc should be a numpy array
if lldict is not None:
backarcgrid = Grid2D(self._backarc, self._Vs30.getGeoDict())
sctx.backarc = backarcgrid.getValue(lats, lons, default=False)
else:
sctx.backarc = self._backarc.copy()
return sctx
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
sctx.vs30measured = np.full_like(dctx.rrup, False, dtype='bool')
sctx.z1pt0 = np.ones_like(dctx.rrup) * 0.05
lmean_ask14, sd_ask14 = ASK14.get_mean_and_stddevs(
sctx, rctx, dctx, IMT, [const.StdDev.TOTAL])
###############################################################################
#
### Now try using mien which uses this... ##
#
## Read in annemarie's ask:
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
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).
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 _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 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])
# List of event IDs.
eventids = [
'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.
def getSitesContext(self, lldict=None, rock_vs30=None):
"""
Create a SitesContext object by sampling the current Sites object.
Args:
lldict: Either
- None, in which case the SitesContext for the complete Sites
grid is returned, or
- A location dictionary (elements are 'lats' and 'lons' and
each is a numpy array). Each element must have the same
shape. In this case the SitesContext for these locaitons is
returned.
rock_vs30: Either
- None, in which case the SitesContext will reflect the Vs30
grid in the Sites instance, or
- A float for the rock Vs30 value, in which case the
SitesContext will be constructed for this constant Vs30
value.
Returns:
SitesContext object.
Raises:
ShakeLibException: When lat/lon input sequences do not share
dimensionality.
""" # noqa
sctx = SitesContext()
if lldict is not None:
lats = lldict['lats']
lons = lldict['lons']
latshape = lats.shape
lonshape = lons.shape
if latshape != lonshape:
msg = 'Input lat/lon arrays must have the same dimensions'
raise ShakeLibException(msg)
if rock_vs30 is not None:
tmp = self._Vs30.getValue(
lats, lons, default=self._defaultVs30)
sctx.vs30 = np.ones_like(tmp) * rock_vs30
else:
sctx.vs30 = self._Vs30.getValue(
lats, lons, default=self._defaultVs30)
sctx.lats = lats
sctx.lons = lons
else:
sctx.lats = self._lats.copy()
sctx.lons = self._lons.copy()
if rock_vs30 is not None:
sctx.vs30 = np.full_like(self._Vs30.getData(), rock_vs30)
else:
sctx.vs30 = self._Vs30.getData().copy()
sctx = Sites._addDepthParameters(sctx)
# For ShakeMap purposes, vs30 measured is always Fales
sctx.vs30measured = np.zeros_like(sctx.vs30, dtype=bool)
# Backarc should be a numpy array
if lldict is not None:
backarcgrid = Grid2D(self._backarc, self._Vs30.getGeoDict())
sctx.backarc = backarcgrid.getValue(lats, lons, default=False)
else:
sctx.backarc = self._backarc.copy()
return sctx
sd_bssa14 = np.array([])
## Run per recording:
for recording_i in range(len(station)):
## 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:
