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_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(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
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_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 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)