def test_get_distances(self):
mag = 6.5
repi = 15.
focal_depth = 10.
comp_rrup, comp_rjb = get_equivalent_distances_west(mag, repi,
focal_depth)
expected_rrup = 7.5506
self.assertAlmostEqual(comp_rrup, expected_rrup, places=2)
expected_rjb = 8.60615
self.assertAlmostEqual(comp_rjb, expected_rjb, places=2)
def test_get_distances(self):
mag = 6.5
repi = 15.
focal_depth = 10.
comp_rrup, comp_rjb = get_equivalent_distances_west(
mag, repi, focal_depth)
expected_rrup = 7.5506
self.assertAlmostEqual(comp_rrup, expected_rrup, places=2)
expected_rjb = 8.60615
self.assertAlmostEqual(comp_rjb, expected_rjb, places=2)
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
""" """
# distances
distsl = copy.copy(dists)
distsl.rjb, distsl.rrup = \
get_equivalent_distances_west(rup.mag, dists.repi)
# get original values
mean, stddevs = super().get_mean_and_stddevs(sites, rup, distsl, imt,
stddev_types)
stds = [np.ones(len(distsl.rjb))*get_sigma(imt)]
return mean, stds
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
""" """
rupl = copy.deepcopy(rup)
rupl.mag -= 0.5
# distances
distsl = copy.copy(dists)
distsl.rjb, distsl.rrup = \
get_equivalent_distances_west(rup.mag, dists.repi)
mean, stddevs = super().get_mean_and_stddevs(sites, rupl, distsl, imt,
stddev_types)
stddevs = [np.ones(len(dists.repi)) * get_sigma(imt)]
return mean, stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
""" """
rupl = copy.deepcopy(rup)
rupl.mag -= 0.5
# distances
distsl = copy.copy(dists)
distsl.rjb, distsl.rrup = \
get_equivalent_distances_west(rup.mag, dists.repi)
mean, stddevs = super().get_mean_and_stddevs(sites, rupl, distsl, imt,
stddev_types)
stddevs = [np.ones(len(dists.repi))*get_sigma(imt)]
return mean, stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
# distances
distsl = copy.copy(dists)
distsl.rjb, distsl.rrup = \
get_equivalent_distances_west(rup.mag, dists.repi)
# get original values
mean, _ = super().get_mean_and_stddevs(sites, rup, distsl, imt,
stddev_types)
# adjust mean values using the reccomended delta (see Atkinson and
# Adams, 2013)
tmp = 0.1+0.0007*distsl.rjb
tmp = np.vstack((tmp, np.ones_like(tmp)*0.3))
delta = np.log(10.**(np.amin(tmp, axis=0)))
mean_adj = mean - delta
stddevs = [np.ones(len(distsl.rjb))*get_sigma(imt)]
return mean_adj, stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
""" """
rupl = copy.deepcopy(rup)
rupl.mag -= 0.5
# distances
distsl = copy.copy(dists)
distsl.rjb, distsl.rrup = \
get_equivalent_distances_west(rup.mag, dists.repi)
mean, stddevs = super().get_mean_and_stddevs(sites, rupl, distsl, imt,
stddev_types)
# Adjust mean values using the reccomended delta (see Atkinson and
# Adams, 2013)
tmp = 0.1+0.0007*distsl.rjb
tmp = np.vstack((tmp, np.ones_like(tmp)*0.3))
delta = np.log(10.**(np.amin(tmp, axis=0)))
mean_adj = mean + delta
stddevs = [np.ones(len(dists.repi))*get_sigma(imt)]
return mean_adj, stddevs
def compute(self, ctx, imts, mean, sig, tau, phi):
# first fix the magnitude for the Oceanic subclass
if self.delta_mag:
ctx = copy.copy(ctx)
ctx.mag += self.delta_mag
# then possibly convert distances from repi to rjb
if self.REQUIRES_DISTANCES == {'repi'}:
ctx = copy.copy(ctx)
ctx.rjb, ctx.rrup = get_equivalent_distances_west(
ctx.mag, ctx.repi)
# set original values
super().compute(ctx, imts, mean, sig, tau, phi)
for m, imt in enumerate(imts):
if self.sgn:
# adjust mean values using the reccomended delta
# (see Atkinson and Adams, 2013)
tmp = 0.1 + 0.0007 * ctx.rjb
tmp = np.vstack((tmp, np.full_like(tmp, 0.3)))
delta = np.log(10.**np.amin(tmp, axis=0))
mean[m] += self.sgn * delta
sig[m] = get_sigma(imt)
