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)