def test_double(self):
fname = gettemp(double_ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
a = Amplifier(self.imtls, df, self.soil_levels)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.985122, 0.979701, 0.975965, 0.96634, 0.922497, 0.886351, 0.790249
],
atol=1E-6)
# poes, [0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79], atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.985122, 0.979701, 0.975965, 0.96634, 0.922497, 0.886351, 0.790249
],
atol=1E-6)
# poes, [0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79], atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.985122, 0.979701, 0.975965, 0.96634, 0.922497, 0.886351, 0.790249
],
atol=1E-6)
# poes, [0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79], atol=1E-6)
# amplify GMFs without sigmas
gmvs = a._amplify_gmvs(b'A', numpy.array([.1, .2, .3]), 'SA(0.5)')
numpy.testing.assert_allclose(gmvs, [.2, .4, .6])
def test_simple(self):
fname = gettemp(simple_ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
a = Amplifier(self.imtls, df, self.soil_levels)
# a.check(self.vs30, vs30_tolerance=1)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.981141, 0.975771, 0.964955, 0.935616, 0.882413, 0.785659,
0.636667
],
atol=1e-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.981141, 0.975771, 0.964955, 0.935616, 0.882413, 0.785659,
0.636667
],
atol=1e-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.981681, 0.976563, 0.967238, 0.940109, 0.890456, 0.799286,
0.686047
],
atol=1e-6)
# Amplify GMFs with sigmas
numpy.random.seed(42)
gmvs = a._amplify_gmvs(b'A', numpy.array([.005, .010, .015]), 'PGA')
numpy.testing.assert_allclose(gmvs, [0.005401, 0.010356, 0.016704],
atol=1E-5)
def test_double(self):
fname = gettemp(double_ampl_func)
aw = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64})
a = Amplifier(self.imtls, aw)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.989, 0.989, 0.985, 0.98, 0.97, 0.94, 0.89, 0.79, 0.69, 0.09, 0.09
],
atol=1E-6)
# amplify GMFs without sigmas
gmvs = a._amplify_gmvs(b'A', numpy.array([.1, .2, .3]), 'SA(0.5)')
numpy.testing.assert_allclose(gmvs, [.2, .4, .6])
def test_simple(self):
fname = gettemp(simple_ampl_func)
aw = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64})
a = Amplifier(self.imtls, aw, self.soil_levels)
a.check(self.vs30, vs30_tolerance=1)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979997, 0.970004, 0.940069, 0.889961, 0.79, 0.690037],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979997, 0.970004, 0.940069, 0.889961, 0.79, 0.690037],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(
poes,
[0.985002, 0.979996, 0.969991, 0.940012, 0.889958, 0.79, 0.690037],
atol=1E-6)
# amplify GMFs with sigmas
numpy.random.seed(42)
gmvs = a._amplify_gmvs(b'A', numpy.array([.005, .010, .015]), 'PGA')
numpy.testing.assert_allclose(gmvs, [0.005307, 0.010093, 0.016804],
atol=1E-5)
def test_gmf_cata(self):
fname = gettemp(cata_ampl_func)
df = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64},
index='ampcode')
imtls = DictArray({'PGA': [numpy.nan]})
a = Amplifier(imtls, df)
numpy.random.seed(42) # must be fixed
gmvs1 = a._amplify_gmvs(b'z1', numpy.array([.1, .2, .3]), 'PGA')
aac(gmvs1, [0.217124, 0.399295, 0.602515], atol=1E-5)
gmvs2 = a._amplify_gmvs(b'z2', numpy.array([.1, .2, .3]), 'PGA')
aac(gmvs2, [0.266652, 0.334187, 0.510845], atol=1E-5)
numpy.random.seed(43) # changing the seed the results change a lot
gmvs1 = a._amplify_gmvs(b'z1', numpy.array([.1, .2, .3]), 'PGA')
aac(gmvs1, [0.197304, 0.293422, 0.399669], atol=1E-5)
gmvs2 = a._amplify_gmvs(b'z2', numpy.array([.1, .2, .3]), 'PGA')
aac(gmvs2, [0.117069, 0.517284, 0.475571], atol=1E-5)
def test_gmf_with_uncertainty(self):
fname = gettemp(gmf_ampl_func)
aw = read_csv(fname, {'ampcode': ampcode_dt, None: numpy.float64})
imtls = {'PGA': self.imls}
a = Amplifier(imtls, aw, self.soil_levels)
res = []
nsim = 10000
numpy.random.seed(42) # must be fixed
for i in range(nsim):
gmvs = a._amplify_gmvs(b'A', numpy.array([.1, .2, .3]), 'PGA')
res.append(list(gmvs))
res = numpy.array(res)
dat = numpy.reshape(numpy.tile([.1, .2, .3], nsim), (nsim, 3))
computed = numpy.std(numpy.log(res / dat), axis=0)
expected = numpy.array([0.3, 0.3, 0.3])
msg = "Computed and expected std do not match"
numpy.testing.assert_almost_equal(computed, expected, 2, err_msg=msg)
def test_simple(self):
#
# MP: checked using hand calculations some values of the poes computed
# considering uncertainty
#
fname = gettemp(simple_ampl_func)
df = read_csv(fname, {
'ampcode': ampcode_dt,
None: numpy.float64
},
index='ampcode')
a = Amplifier(self.imtls, df, self.soil_levels)
a.check(self.vs30, vs30_tolerance=1)
poes = a.amplify_one(b'A', 'SA(0.1)', self.hcurve[1]).flatten()
numpy.testing.assert_allclose(poes, [
0.985008, 0.980001, 0.970019, 0.94006, 0.890007, 0.790198, 0.690201
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.2)', self.hcurve[2]).flatten()
numpy.testing.assert_allclose(poes, [
0.985008, 0.980001, 0.970019, 0.94006, 0.890007, 0.790198, 0.690201
],
atol=1E-6)
poes = a.amplify_one(b'A', 'SA(0.5)', self.hcurve[3]).flatten()
numpy.testing.assert_allclose(poes, [
0.985109, 0.980022, 0.970272, 0.940816, 0.890224, 0.792719,
0.692719
],
atol=1E-6)
# Amplify GMFs with sigmas
numpy.random.seed(42)
gmvs = a._amplify_gmvs(b'A', numpy.array([.005, .010, .015]), 'PGA')
numpy.testing.assert_allclose(gmvs, [0.005401, 0.010356, 0.016704],
atol=1E-5)
