本文整理匯總了Python中openquake.hazardlib.gsim.base.DistancesContext.rhypo方法的典型用法代碼示例。如果您正苦於以下問題：Python DistancesContext.rhypo方法的具體用法？Python DistancesContext.rhypo怎麽用？Python DistancesContext.rhypo使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類openquake.hazardlib.gsim.base.DistancesContext的用法示例。
在下文中一共展示了DistancesContext.rhypo方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_dist_not_in_increasing_order
# 需要導入模塊: from openquake.hazardlib.gsim.base import DistancesContext [as 別名]
# 或者: from openquake.hazardlib.gsim.base.DistancesContext import rhypo [as 別名]
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])示例2: test_mag_dist_outside_range
# 需要導入模塊: from openquake.hazardlib.gsim.base import DistancesContext [as 別名]
# 或者: from openquake.hazardlib.gsim.base.DistancesContext import rhypo [as 別名]
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)示例3: test_rhypo_smaller_than_15
# 需要導入模塊: from openquake.hazardlib.gsim.base import DistancesContext [as 別名]
# 或者: from openquake.hazardlib.gsim.base.DistancesContext import rhypo [as 別名]
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)示例4:
# 需要導入模塊: from openquake.hazardlib.gsim.base import DistancesContext [as 別名]
# 或者: from openquake.hazardlib.gsim.base.DistancesContext import rhypo [as 別名]
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
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])
###############################################################################