def setUp(self):
"""
Open the hdf5 file
"""
self.fle = h5py.File(self.TABLE_FILE)
self.amp_table = AmplificationTable(self.fle["Amplification"],
self.fle["Mw"][:],
self.fle["Distances"][:])
def setUp(self):
"""
Open the hdf5 file
"""
self.fle = h5py.File(self.TABLE_FILE)
self.amp_table = AmplificationTable(self.fle["Amplification"],
self.fle["Mw"][:],
self.fle["Distances"][:])
def test_unsupported_parameter(self):
"""
Tests instantiation with a bad input
"""
with self.assertRaises(ValueError) as ve:
AmplificationTable(self.fle["Amplification"], None, None)
self.assertEqual(str(ve.exception),
"Amplification parameter Bad Value not recognised!")
class AmplificationTableSiteTestCase(unittest.TestCase):
"""
Tests the amplification tables for a site parameter
"""
TABLE_FILE = os.path.join(BASE_DATA_PATH, "model_amplification_site.hdf5")
IDX = 0
def setUp(self):
"""
Open the hdf5 file
"""
self.fle = h5py.File(self.TABLE_FILE)
self.amp_table = AmplificationTable(self.fle["Amplification"],
self.fle["Mw"][:],
self.fle["Distances"][:])
def test_instantiation(self):
"""
Tests the setup and loading of data from file to memory
"""
# Check setup
# 1. Shape
self.assertTupleEqual(self.amp_table.shape, (10, 3, 5, 2))
# 2. Parameter
self.assertEqual(self.amp_table.parameter, "vs30")
# 3. Element
self.assertEqual(self.amp_table.element, "Sites")
# 4. Interpolation values
np.testing.assert_array_almost_equal(self.amp_table.values,
np.array([400.0, 1000.0]))
# 5. Periods
np.testing.assert_array_almost_equal(self.amp_table.periods,
np.array([0.1, 0.5, 1.0]))
# 6. Means and Standard Deviations
expected_mean, expected_sigma = self._build_mean_and_stddev_table()
for key in self.amp_table.mean:
np.testing.assert_array_almost_equal(self.amp_table.mean[key],
expected_mean[key])
np.testing.assert_array_almost_equal(
self.amp_table.sigma["Total"][key],
expected_sigma["Total"][key])
def _build_mean_and_stddev_table(self):
"""
Builds the expected mean and standard deviation tables
"""
expected_means = {
"PGA": np.ones([10, 1, 5, 2]),
"PGV": np.ones([10, 1, 5, 2]),
"SA": np.ones([10, 3, 5, 2])
}
# For second level revise values
expected_means["PGA"][:, :, :, self.IDX] *= 1.5
expected_means["PGV"][:, :, :, self.IDX] *= 0.5
expected_means["SA"][:, 0, :, self.IDX] *= 1.5
expected_means["SA"][:, 1, :, self.IDX] *= 2.0
expected_means["SA"][:, 2, :, self.IDX] *= 0.5
expected_sigma = {
const.StdDev.TOTAL: {
"PGA": np.ones([10, 1, 5, 2]),
"PGV": np.ones([10, 1, 5, 2]),
"SA": np.ones([10, 3, 5, 2])
}
}
expected_sigma[const.StdDev.TOTAL]["PGA"][:, :, :, self.IDX] *= 0.8
expected_sigma[const.StdDev.TOTAL]["PGV"][:, :, :, self.IDX] *= 0.8
expected_sigma[const.StdDev.TOTAL]["SA"][:, :, :, self.IDX] *= 0.8
return expected_means, expected_sigma
def test_get_set(self):
"""
Test that the set function operates correctly
"""
self.assertSetEqual(self.amp_table.get_set(), {"vs30"})
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_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_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 tearDown(self):
"""
Close the hdf5 file
"""
self.fle.close()
class AmplificationTableSiteTestCase(unittest.TestCase):
"""
Tests the amplification tables for a site parameter
"""
TABLE_FILE = os.path.join(BASE_DATA_PATH, "model_amplification_site.hdf5")
IDX = 0
def setUp(self):
"""
Open the hdf5 file
"""
self.fle = h5py.File(self.TABLE_FILE)
self.amp_table = AmplificationTable(self.fle["Amplification"],
self.fle["Mw"][:],
self.fle["Distances"][:])
def test_instantiation(self):
"""
Tests the setup and loading of data from file to memory
"""
# Check setup
# 1. Shape
self.assertTupleEqual(self.amp_table.shape, (10, 3, 5, 2))
# 2. Parameter
self.assertEqual(self.amp_table.parameter, "vs30")
# 3. Element
self.assertEqual(self.amp_table.element, "Sites")
# 4. Interpolation values
np.testing.assert_array_almost_equal(self.amp_table.values,
np.array([400.0, 1000.0]))
# 5. Periods
np.testing.assert_array_almost_equal(self.amp_table.periods,
np.array([0.1, 0.5, 1.0]))
# 6. Means and Standard Deviations
expected_mean, expected_sigma = self._build_mean_and_stddev_table()
for key in self.amp_table.mean:
np.testing.assert_array_almost_equal(self.amp_table.mean[key],
expected_mean[key])
np.testing.assert_array_almost_equal(
self.amp_table.sigma["Total"][key],
expected_sigma["Total"][key])
def _build_mean_and_stddev_table(self):
"""
Builds the expected mean and standard deviation tables
"""
expected_means = {
"PGA": np.ones([10, 1, 5, 2]),
"PGV": np.ones([10, 1, 5, 2]),
"SA": np.ones([10, 3, 5, 2])
}
# For second level revise values
expected_means["PGA"][:, :, :, self.IDX] *= 1.5
expected_means["PGV"][:, :, :, self.IDX] *= 0.5
expected_means["SA"][:, 0, :, self.IDX] *= 1.5
expected_means["SA"][:, 1, :, self.IDX] *= 2.0
expected_means["SA"][:, 2, :, self.IDX] *= 0.5
expected_sigma = {const.StdDev.TOTAL: {
"PGA": np.ones([10, 1, 5, 2]),
"PGV": np.ones([10, 1, 5, 2]),
"SA": np.ones([10, 3, 5, 2])
}}
expected_sigma[const.StdDev.TOTAL]["PGA"][:, :, :, self.IDX] *= 0.8
expected_sigma[const.StdDev.TOTAL]["PGV"][:, :, :, self.IDX] *= 0.8
expected_sigma[const.StdDev.TOTAL]["SA"][:, :, :, self.IDX] *= 0.8
return expected_means, expected_sigma
def test_get_set(self):
"""
Test that the set function operates correctly
"""
self.assertSetEqual(self.amp_table.get_set(), {"vs30"})
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_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_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 tearDown(self):
"""
Close the hdf5 file
"""
self.fle.close()