def get_mean_and_stddevs(self, sx, rx, dx, imt, stddev_types, fd=None):
"""
See superclass
`method <http://docs.openquake.org/oq-hazardlib/master/gsim/index.html#openquake.hazardlib.gsim.base.GroundShakingIntensityModel.get_mean_and_stddevs>`__
for parameter definitions. The only acceptable IMT is MMI.
Additional subclass argument is "fd", which is the directivity
amplification factor in natural log units. This is optional,
and must be a numpy array with the same dimentions as the
sites and is added to the ground motions before conversion to
MMI.
Returns:
ndarray, list of ndarray:
mmi (ndarray): Ground motions predicted by the MultiGMPE using
the supplied parameters are converted to MMI using the GMICE.
mmi_sd (list of ndarrays): The uncertainty of the combined
prediction/conversion process. The prediction uncertainty will
typically be either OpenQuake's TOTAL or INTRA_EVENT. But can
be any set that the MultiGMPE supports. See the ShakeMap manual
for a detailed discussion of the way the uncertainty is computed.
""" # noqa
if imt != MMI():
raise ValueError("imt must be MMI")
#
# Get the mean ground motions and stddev for the preferred IMT
#
mgm, sdev = self.gmpe.get_mean_and_stddevs(sx, rx, dx, self.imt,
stddev_types)
if fd is not None:
mgm = mgm + fd
#
# Get the MMI and the dMMI/dPGM from the GMICE
#
if hasattr(dx, 'rrup'):
dist4gmice = dx.rrup
else:
dist4gmice = dx.rhypo
mmi, dmda = self.gmice.getMIfromGM(mgm, self.imt, dist4gmice, rx.mag)
#
# Compute the uncertainty of the combined prediction/conversion
# Total and intra-event uncertanty are inflated by the uncertainty
# of the conversion; inter-event uncertainty is not.
#
ntypes = len(stddev_types)
nsd = len(sdev)
mmi_sd = [None] * nsd
gm2mi_var = (self.gmice.getGM2MIsd()[self.imt])**2
dmda *= dmda
for i in range(nsd):
gm_var_in_mmi = dmda * sdev[i]**2
if stddev_types[i % ntypes] == const.StdDev.INTER_EVENT:
mmi_sd[i] = np.sqrt(gm_var_in_mmi)
else:
mmi_sd[i] = np.sqrt(gm2mi_var + gm_var_in_mmi)
return mmi, mmi_sd
def setUp(self):
# Set parameters
self.sites = Dummy.get_site_collection(
4, amplfactor=[-1.0, 1.5, 0.00, -1.99])
self.rup = Dummy.get_rupture(mag=6.0, hypo_depth=10)
self.dists = DistancesContext()
self.dists.rhypo = np.array([1., 10., 30., 70.])
self.dists.repi = np.array([1., 10., 30., 70.])
self.imt = MMI()
def setUp(self):
self.ctx = ctx = RuptureContext()
ctx.mag = 6.0
ctx.hypo_depth = 10.
sites = Dummy.get_site_collection(4,
amplfactor=[-1.0, 1.5, 0.00, -1.99])
for name in sites.array.dtype.names:
setattr(ctx, name, sites[name])
ctx.rhypo = np.array([1., 10., 30., 70.])
ctx.repi = np.array([1., 10., 30., 70.])
self.imt = MMI()
def test_wald99():
gmice = Wald99()
df = {'PGA': amps_in,
'PGV': amps_in + np.log(100)}
mi = gmice.getPreferredMI(df)
mi_target = np.array(
[3.18167182, 4.23133858, 5.68946656, 6.84666436, 7.47561075,
7.90914817, 8.24542592, 9.29])
if do_test is True:
np.testing.assert_allclose(mi, mi_target)
else:
print(repr(mi))
pga_amps_in_nan = np.log(np.array(
[0.01, 0.03, 0.1, np.nan, 0.3, 0.4, 0.5, 1.0]))
pgv_amps_in_nan = np.log(np.array(
[0.01, 0.03, 0.1, 0.2, np.nan, 0.4, 0.5, 1.0])) + np.log(100)
pga_amps_in_nan[5] = np.nan
pgv_amps_in_nan[5] = np.nan
df = {'PGA': pga_amps_in_nan,
'PGV': pgv_amps_in_nan}
mi = gmice.getPreferredMI(df)
mi_target = np.array(
[3.18167182, 4.23133858, 5.68946656, 6.86457408, 7.37577236,
np.nan, 8.24542592, 9.29])
if do_test is True:
np.testing.assert_allclose(mi, mi_target)
else:
print(repr(mi))
mi, dmda = gmice.getMIfromGM(amps_in, PGA(), dists=None, mag=None)
mi_target = np.array(
[3.18167182, 4.23133858, 5.62950857, 6.73127835, 7.37577236,
7.83304814, 8.18773878, 9.28950857])
if do_test is True:
np.testing.assert_allclose(mi, mi_target)
assert((set(dmda) - dmda_target[PGA()]) == set())
else:
print(repr(mi))
mi, dmda = gmice.getMIfromGM(
amps_in + np.log(100), PGV(), dists=None, mag=None)
mi_target = np.array(
[3.4, 4.40195463, 5.82, 6.86457408, 7.47561075,
7.90914817, 8.24542592, 9.29])
if do_test is True:
np.testing.assert_allclose(mi, mi_target)
assert((set(dmda) - dmda_target[PGV()]) == set())
else:
print(repr(mi))
amps, dadm = gmice.getGMfromMI(mmi_in, PGA(), dists=None, mag=None)
amps_target = np.array(
[-5.84194287, -4.79531328, -3.7486837, -2.69862254, -1.9436766,
-1.25164283, -0.74834554, -0.1821361])
if do_test is True:
np.testing.assert_allclose(amps, amps_target)
assert((set(dadm) - dadm_target[PGA()]) == set())
else:
print(repr(amps))
amps, dadm = gmice.getGMfromMI(mmi_in, PGV(), dists=None, mag=None)
amps_target = np.array(
[-1.53505673, -0.43858764, 0.65788146, 1.75845836, 2.55474139,
3.2846675, 3.81552285, 4.41273512])
if do_test is True:
np.testing.assert_allclose(amps, amps_target)
assert((set(dadm) - dadm_target[PGV()]) == set())
else:
print(repr(amps))
if do_test is True:
sdd = gmice.getGM2MIsd()
assert sdd[PGA()] == 1.08
assert sdd[PGV()] == 0.98
sdd = gmice.getMI2GMsd()
lnten = np.log(10.0)
np.testing.assert_allclose(sdd[PGA()], 0.295 * lnten)
np.testing.assert_allclose(sdd[PGV()], 0.282 * lnten)
nm = gmice.getName()
assert nm == 'Wald et al. (1999)'
sc = gmice.getScale()
assert sc == 'scale_wald99.ps'
mm = gmice.getMinMax()
assert mm == (1.0, 10.0)
dt = gmice.getDistanceType()
assert dt == 'rrup'
#
# This should fail because MMI() is not a valid argument to
# getMIfromGM
#
with pytest.raises(ValueError) as e: # noqa
mi, dmda = gmice.getMIfromGM(amps_in, MMI(), dists=None, mag=None)
def test_ak07():
gmice = AK07()
mi, dmda = gmice.getMIfromGM(amps_in, PGA(), dists=None, mag=None)
mi_target = np.array(
[4.02841992026, 4.69161846432, 6.23592624018, 7.46713892245,
8.18735217199, 8.69835160472, 9.09471355792])
np.testing.assert_allclose(mi, mi_target)
mi, dmda = gmice.getMIfromGM(
amps_in + np.log(100), PGV(), dists=dists, mag=None)
mi_target = np.array(
[4.37, 4.99980005623, 6.57, 7.48212088686, 8.0156774018,
8.39424177372, 8.68787911314])
np.testing.assert_allclose(mi, mi_target)
mi, dmda = gmice.getMIfromGM(amps_in, SA(0.3), dists=None, mag=3.0)
mi_target = np.array(
[3.74866985004, 4.39755475646, 5.26034166627, 6.33200845084,
6.95889333307, 7.4036752354, 7.74867488171])
np.testing.assert_allclose(mi, mi_target)
mi, dmda = gmice.getMIfromGM(amps_in, SA(1.0), dists=dists, mag=7.5)
mi_target = np.array(
[3.52702354769, 4.07617250928, 5.55842357177, 6.46666805811,
7.00909852304, 7.39358539638, 7.67946993475])
np.testing.assert_allclose(mi, mi_target)
mi, dmda = gmice.getMIfromGM(amps_in, SA(3.0), dists=None, mag=None)
mi_target = np.array(
[4.99925301952, 6.3963707863, 7.96500702214, 8.86809700913,
9.3963707863, 9.77118699612, 10.])
np.testing.assert_allclose(mi, mi_target)
amps, dadm = gmice.getGMfromMI(mmi_in, PGA(), dists=None, mag=7.0)
amps_target = np.log(np.array(
[0.000347300247926, 0.00182024377197, 0.00954012388176,
0.0498674941261, 0.0979977440572, 0.182039122534,
0.285603651352]))
np.testing.assert_allclose(amps, amps_target)
amps, dadm = gmice.getGMfromMI(mmi_in, PGV(), dists=dists, mag=None)
amps_target = np.log(np.array(
[0.0160156826445, 0.0916476244071, 0.524441401963,
3.03283082017, 7.54897155245, 17.4150246057,
31.9853049046]))
np.testing.assert_allclose(amps, amps_target)
amps, dadm = gmice.getGMfromMI(mmi_in, SA(0.3), dists=None, mag=6.0)
amps_target = np.log(np.array(
[0.000517861166862, 0.00281518839281, 0.0153038810286,
0.0845026480232, 0.183632256171, 0.374056955014,
0.627562284541]))
np.testing.assert_allclose(amps, amps_target)
amps, dadm = gmice.getGMfromMI(mmi_in, SA(1.0), dists=dists, mag=8.0)
amps_target = np.log(np.array(
[0.000508056775718, 0.00367375892063, 0.0258564132795,
0.0636582041631, 0.159533013524, 0.371822895172,
0.694260679175]))
np.testing.assert_allclose(amps, amps_target)
amps, dadm = gmice.getGMfromMI(mmi_in, SA(3.0), dists=None, mag=5.0)
amps_target = np.log(np.array(
[0.0000473148708829, 0.000281962568289, 0.00168029392098,
0.0102722203276, 0.0258026508623, 0.0600248374471,
0.110916883717]))
np.testing.assert_allclose(amps, amps_target)
amps, dadm = gmice.getGMfromMI(mmi_in, PGA(), dists=None, mag=7.0)
amps_target = np.log(np.array(
[0.000347300247926, 0.00182024377197, 0.00954012388176,
0.0498674941261, 0.0979977440572, 0.182039122534,
0.285603651352]))
# print(repr(100 * np.exp(amps)))
np.testing.assert_allclose(amps, amps_target)
sdd = gmice.getGM2MIsd()
assert sdd[PGA()] == 0.89
assert sdd[PGV()] == 0.76
assert sdd[SA(0.3)] == 0.79
assert sdd[SA(1.0)] == 0.73
assert sdd[SA(3.0)] == 0.72
sdd = gmice.getMI2GMsd()
assert abs(sdd[PGA()] - 1.312473503006606) < 0.0000001
assert abs(sdd[PGV()] - 1.197344248356904) < 0.0000001
assert abs(sdd[SA(0.3)] - 1.450628608586249) < 0.0000001
assert abs(sdd[SA(1.0)] - 1.312473503006606) < 0.0000001
assert abs(sdd[SA(3.0)] - 1.865093925325177) < 0.0000001
nm = gmice.getName()
assert nm == 'Atkinson and Kaka (2007)'
sc = gmice.getScale()
assert sc == 'scale_ak07.ps'
mm = gmice.getMinMax()
assert mm == (1.0, 10.0)
dt = gmice.getDistanceType()
assert dt == 'rrup'
#
# This should fail
#
with pytest.raises(ValueError) as e: # noqa
mi, dmda = gmice.getMIfromGM(amps_in, MMI(), dists=None, mag=None)
def test_virtualipe():
#
# Set up the GMPE, IPE, and GMICE
#
gmpe_cy14 = ChiouYoungs2014()
gmpe = MultiGMPE.from_list([gmpe_cy14], [1.0])
gmice = WGRW12()
ipe = VirtualIPE.fromFuncs(gmpe, gmice)
#
# Use the Calexico event info
#
homedir = os.path.dirname(os.path.abspath(__file__))
datadir = os.path.abspath(
os.path.join(homedir, 'virtualipe_data', 'Calexico', 'input'))
#
# Read the event, origin, and rupture files and produce Rupture and Origin
# objects
#
# inputfile = os.path.join(datadir, 'stationlist_dat.xml')
# dyfifile = os.path.join(datadir, 'ciim3_dat.xml')
eventfile = os.path.join(datadir, 'event.xml')
rupturefile = os.path.join(datadir, 'wei_fault.txt')
origin_obj = Origin.fromFile(eventfile)
rupture_obj = get_rupture(origin_obj, rupturefile)
rx = rupture_obj.getRuptureContext([gmpe])
rx.rake = 45.
smdx = 0.0083333333
smdy = 0.0083333333
lonspan = 6.0
latspan = 4.0
vs30filename = os.path.join(datadir, '..', 'vs30', 'vs30.grd')
sites_obj_grid = Sites.fromCenter(rx.hypo_lon,
rx.hypo_lat,
lonspan,
latspan,
smdx,
smdy,
defaultVs30=760.0,
vs30File=vs30filename,
vs30measured_grid=None,
padding=False,
resample=False)
npts = 200
lats = np.empty(npts)
lons = np.empty(npts)
depths = np.zeros(npts)
for i in range(npts):
lats[i] = rx.hypo_lat
lons[i] = rx.hypo_lon + i * 0.01
lldict = {'lats': lats, 'lons': lons}
sx = sites_obj_grid.getSitesContext(lldict=lldict, rock_vs30=760.0)
dobj = Distance(gmpe, lons, lats, depths, rupture_obj)
dx = dobj.getDistanceContext()
sd_types = [oqconst.StdDev.TOTAL]
mmi_const_vs30, mmi_sd_const_vs30 = \
ipe.get_mean_and_stddevs(sx, rx, dx, MMI(), sd_types)
# These prints are just so a human can examine the outputs
# print(mmi_const_vs30)
# print(mmi_sd_const_vs30)
sx = sites_obj_grid.getSitesContext(lldict=lldict)
mmi_variable_vs30, mmi_sd_variable_vs30 = \
ipe.get_mean_and_stddevs(sx, rx, dx, MMI(), sd_types)
# print(mmi_variable_vs30)
# print(mmi_sd_variable_vs30)
sd_types = [
oqconst.StdDev.TOTAL, oqconst.StdDev.INTRA_EVENT,
oqconst.StdDev.INTER_EVENT
]
mmi_variable_vs30_intra, mmi_sd_variable_vs30_intra = \
ipe.get_mean_and_stddevs(sx, rx, dx, MMI(), sd_types)
# print(mmi_variable_vs30_intra)
# print(mmi_sd_variable_vs30_intra)
# assert(0) # Assert causes test to fail and prints to be displayed
#
# Try with PGA
#
gmpe.DEFINED_FOR_INTENSITY_MEASURE_TYPES.remove(PGV)
gmpe.ALL_GMPES_HAVE_PGV = False
ipe = VirtualIPE.fromFuncs(gmpe, gmice)
mmi_pga, mmi_sd_pga = \
ipe.get_mean_and_stddevs(sx, rx, dx, MMI(), sd_types)
#
# Try with SA(1.0)
#
gmpe.DEFINED_FOR_INTENSITY_MEASURE_TYPES.remove(PGA)
ipe = VirtualIPE.fromFuncs(gmpe, gmice)
mmi_psa, mmi_sd_psa = \
ipe.get_mean_and_stddevs(sx, rx, dx, MMI(), sd_types)
#
# This should raise an exception because the IMT isn't MMI
#
with pytest.raises(ValueError) as e:
mmi_psa, mmi_sd_psa = \
ipe.get_mean_and_stddevs(sx, rx, dx, PGA(), sd_types)
#
# This should raise an exception because no valid IMTs are available
#
gmpe.DEFINED_FOR_INTENSITY_MEASURE_TYPES.remove(SA)
with pytest.raises(ShakeLibException) as e: # noqa
ipe = VirtualIPE.fromFuncs(gmpe, gmice)
#
# Now do a GMPE that uses Rjb instead of Rrup
#
gmpe_ba14 = BooreEtAl2014()
gmpe = MultiGMPE.from_list([gmpe_ba14], [1.0])
ipe = VirtualIPE.fromFuncs(gmpe, gmice)
rx = rupture_obj.getRuptureContext([gmpe])
rx.rake = 45.
dobj = Distance(gmpe, lons, lats, depths, rupture_obj)
dx = dobj.getDistanceContext()
mmi_rjb, mmi_sd_rjb = \
ipe.get_mean_and_stddevs(sx, rx, dx, MMI(), sd_types)
#
# Test the results against a known standard
#
savefile = os.path.abspath(
os.path.join(homedir, 'virtualipe_data', 'Calexico', 'virtualipe_test',
'savefile.npz'))
#
# If things change, set remake_save to True, and it will rebuild the
# saved data file against which the comparisons are done
# Remember to set this back to False once you've remade the test datafile
#
remake_save = False
if remake_save:
np.savez_compressed(
savefile,
mmi_const_vs30=mmi_const_vs30,
mmi_sd_const_vs30=mmi_sd_const_vs30[0],
mmi_variable_vs30=mmi_variable_vs30,
mmi_sd_variable_vs30=mmi_sd_variable_vs30[0],
mmi_variable_vs30_intra=mmi_variable_vs30_intra,
mmi_sd_variable_vs30_total=mmi_sd_variable_vs30_intra[0],
mmi_sd_variable_vs30_intra=mmi_sd_variable_vs30_intra[1],
mmi_sd_variable_vs30_inter=mmi_sd_variable_vs30_intra[2],
mmi_pga=mmi_pga,
mmi_sd_pga=mmi_sd_pga[0],
mmi_psa=mmi_psa,
mmi_sd_psa=mmi_sd_psa[0],
mmi_rjb=mmi_rjb,
mmi_sd_rjb=mmi_sd_rjb[0])
td = np.load(savefile)
assert (np.allclose(td['mmi_const_vs30'], mmi_const_vs30))
assert (np.allclose(td['mmi_sd_const_vs30'], mmi_sd_const_vs30[0]))
assert (np.allclose(td['mmi_variable_vs30'], mmi_variable_vs30))
assert (np.allclose(td['mmi_sd_variable_vs30'], mmi_sd_variable_vs30[0]))
assert (np.allclose(td['mmi_variable_vs30_intra'],
mmi_variable_vs30_intra))
assert (np.allclose(td['mmi_sd_variable_vs30_total'],
mmi_sd_variable_vs30_intra[0]))
assert (np.allclose(td['mmi_sd_variable_vs30_intra'],
mmi_sd_variable_vs30_intra[1]))
assert (np.allclose(td['mmi_sd_variable_vs30_inter'],
mmi_sd_variable_vs30_intra[2]))
assert (np.allclose(td['mmi_pga'], mmi_pga))
assert (np.allclose(td['mmi_sd_pga'], mmi_sd_pga[0]))
assert (np.allclose(td['mmi_psa'], mmi_psa))
assert (np.allclose(td['mmi_sd_psa'], mmi_sd_psa[0]))
assert (np.allclose(td['mmi_rjb'], mmi_rjb))
assert (np.allclose(td['mmi_sd_rjb'], mmi_sd_rjb[0]))
# The total uncertainties should be greater than the intra-event
assert (np.all(mmi_sd_variable_vs30[0] > mmi_sd_variable_vs30_intra[1]))
# The combined intra and inter-event uncertainty should be equal
# to the total
tot = np.sqrt(mmi_sd_variable_vs30_intra[1]**2 +
mmi_sd_variable_vs30_intra[2]**2)
assert (np.allclose(tot, mmi_sd_variable_vs30_intra[0], rtol=1e-2))
def _parse_csv_line(headers, values):
"""
Parse a single line from data file.
:param headers:
A list of header names, the strings from the first line of csv file.
:param values:
A list of values of a single row to parse.
:returns:
A tuple of the following values (in specified order):
sctx
An instance of :class:`openquake.hazardlib.gsim.base.SitesContext`
with attributes populated by the information from in row in a form
of single-element numpy arrays.
rctx
An instance of
:class:`openquake.hazardlib.gsim.base.RuptureContext`.
dctx
An instance of
:class:`openquake.hazardlib.gsim.base.DistancesContext`.
stddev_types
An empty list, if the ``result_type`` column says "MEAN"
for that row, otherwise it is a list with one item --
a requested standard deviation type.
expected_results
A dictionary mapping IMT-objects to one-element arrays of expected
result values. Those results represent either standard deviation
or mean value of corresponding IMT depending on ``result_type``.
result_type
A string literal, one of ``'STDDEV'`` or ``'MEAN'``. Value
is taken from column ``result_type``.
"""
rctx = RuptureContext()
sctx = SitesContext()
dctx = DistancesContext()
expected_results = {}
stddev_types = result_type = damping = None
for param, value in zip(headers, values):
if param == 'result_type':
value = value.upper()
if value.endswith('_STDDEV'):
# the row defines expected stddev results
result_type = 'STDDEV'
stddev_types = [getattr(const.StdDev, value[:-len('_STDDEV')])]
else:
# the row defines expected exponents of mean values
assert value == 'MEAN'
stddev_types = []
result_type = 'MEAN'
elif param == 'damping':
damping = float(value)
elif param.startswith('site_'):
# value is sites context object attribute
if (param == 'site_vs30measured') or (param == 'site_backarc'):
value = float(value) != 0
else:
value = float(value)
setattr(sctx, param[len('site_'):], numpy.array([value]))
elif param.startswith('dist_'):
# value is a distance measure
value = float(value)
setattr(dctx, param[len('dist_'):], numpy.array([value]))
elif param.startswith('rup_'):
# value is a rupture context attribute
value = float(value)
setattr(rctx, param[len('rup_'):], value)
elif param == 'component_type':
pass
else:
# value is the expected result (of result_type type)
value = float(value)
if param == 'pga':
imt = PGA()
elif param == 'pgv':
imt = PGV()
elif param == 'pgd':
imt = PGD()
elif param == 'cav':
imt = CAV()
elif param == 'mmi':
imt = MMI()
elif param == "arias":
imt = IA()
elif param == "rsd595":
imt = RSD595()
elif param == "rsd575":
imt = RSD575()
elif param == "rsd2080":
imt = RSD2080()
else:
period = float(param)
assert damping is not None
imt = SA(period, damping)
expected_results[imt] = numpy.array([value])
assert result_type is not None
return sctx, rctx, dctx, stddev_types, expected_results, result_type
def test_wgrw12():
gmice = WGRW12()
mi, dmda = gmice.getMIfromGM(amps_in, PGA(), dists=None, mag=None)
mi_target = np.array(
[3.31708696, 4.05662491, 5.76917533, 6.88298631, 7.53452397,
7.9967973, 8.35536434])
np.testing.assert_allclose(mi, mi_target)
assert((set(dmda) - dmda_target[PGA()]) == set())
mi, dmda = gmice.getMIfromGM(
amps_in + np.log(100), PGV(), dists=dists, mag=None)
mi_target = np.array(
[3.78, 4.48136824, 6.05, 7.00125479, 7.55770316,
7.95250957, 8.25874521])
np.testing.assert_allclose(mi, mi_target)
assert((set(dmda) - dmda_target[PGV()]) == set())
mi, dmda = gmice.getMIfromGM(amps_in, SA(0.3), dists=None, mag=3.0)
mi_target = np.array(
[2.93592062, 3.74225554, 4.62592062, 5.34177387, 6.07079169,
6.58803805, 6.98924551])
np.testing.assert_allclose(mi, mi_target)
assert((set(dmda) - dmda_target[SA(0.3)]) == set())
mi, dmda = gmice.getMIfromGM(amps_in, SA(1.0), dists=dists, mag=7.5)
mi_target = np.array(
[3.49070724, 4.3808733, 5.63884012, 6.26430362, 6.49369295,
6.66102468, 6.94206372])
np.testing.assert_allclose(mi, mi_target)
assert((set(dmda) - dmda_target[SA(1.0)]) == set())
mi, dmda = gmice.getMIfromGM(amps_in, SA(3.0), dists=None, mag=None)
mi_target = np.array(
[4.97492371, 6.41105869, 7.98492371, 8.891024,
9.42105869, 9.79712429, 10.])
np.testing.assert_allclose(mi, mi_target)
assert((set(dmda) - dmda_target[SA(3.0)]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, PGA(), dists=None, mag=7.0)
amps_target = np.log(np.array(
[0.14134045, 0.6243495, 2.75796695, 6.19604804,
13.07492182, 25.92605261, 42.65329774]) / 100.0)
np.testing.assert_allclose(amps, amps_target)
assert((set(dadm) - dadm_target[PGA()]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, PGV(), dists=dists, mag=None)
amps_target = np.log(np.array(
[0.06153407, 0.29470517, 1.41143169, 4.65287643,
11.15496866, 24.86392117, 44.53835548]))
np.testing.assert_allclose(amps, amps_target)
assert((set(dadm) - dadm_target[PGV()]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, SA(0.3), dists=None, mag=6.0)
amps_target = np.log(np.array(
[0.27938354, 1.09123124, 4.26218963, 16.53773087,
32.23524628, 59.43352318, 92.74023471]) / 100.0)
np.testing.assert_allclose(amps, amps_target)
assert((set(dadm) - dadm_target[SA(0.3)]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, SA(1.0), dists=dists, mag=8.0)
amps_target = np.log(np.array(
[1.02985637e-01, 3.65288187e-01, 1.67836836e+00,
7.32931235e+00, 2.37600759e+01, 6.64349034e+01,
1.25388693e+02]) / 100.0)
np.testing.assert_allclose(amps, amps_target)
assert((set(dadm) - dadm_target[SA(1.0)]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, SA(3.0), dists=None, mag=5.0)
amps_target = np.log(np.array(
[2.89282689e-03, 2.07025242e-02, 1.48157675e-01,
1.01936799e+00, 2.55271358e+00, 5.92175716e+00,
1.09202184e+01]) / 100.0)
np.testing.assert_allclose(amps, amps_target)
assert((set(dadm) - dadm_target[SA(3.0)]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, PGA(), dists=None, mag=7.0)
amps_target = np.log(np.array(
[0.14134045, 0.6243495, 2.75796695, 6.19604804,
13.07492182, 25.92605261, 42.65329774]) / 100.0)
np.testing.assert_allclose(amps, amps_target)
assert((set(dadm) - dadm_target[PGA()]) == set())
sdd = gmice.getGM2MIsd()
assert sdd[PGA()] == 0.66
assert sdd[PGV()] == 0.63
assert sdd[SA(0.3)] == 0.82
assert sdd[SA(1.0)] == 0.75
assert sdd[SA(3.0)] == 0.89
sdd = gmice.getMI2GMsd()
lnten = np.log(10.0)
np.testing.assert_allclose(sdd[PGA()], np.log10(2.238721) * lnten)
np.testing.assert_allclose(sdd[PGV()], np.log10(2.39883291) * lnten)
np.testing.assert_allclose(sdd[SA(0.3)], np.log10(2.7542287) * lnten)
np.testing.assert_allclose(sdd[SA(1.0)], np.log10(2.951209) * lnten)
np.testing.assert_allclose(sdd[SA(3.0)], np.log10(4.365158) * lnten)
nm = gmice.getName()
assert nm == 'Worden et al. (2012)'
sc = gmice.getScale()
assert sc == 'scale_wgrw12.ps'
mm = gmice.getMinMax()
assert mm == (1.0, 10.0)
dt = gmice.getDistanceType()
assert dt == 'rrup'
assert gmice.supports('PGA')
assert gmice.supports('PGV')
assert gmice.supports('SA(0.3)')
assert gmice.supports('SA(1.0)')
assert gmice.supports('SA(3.0)')
try:
assert gmice.supports('SA(10.0)')
assert 1 == 2
except AssertionError:
assert 1 == 1
#
# This should fail
#
with pytest.raises(ValueError) as e: # noqa
mi, dmda = gmice.getMIfromGM(amps_in, MMI(), dists=None, mag=None)
def test_fm11():
gmice = FM11()
mi, dmda = gmice.getMIfromGM(amps_in, PGA(), dists=None, mag=None)
mi_target = np.array(
[4.238506, 5.469479, 6.818506, 7.595163, 8.049479, 8.371821, 8.621849])
np.testing.assert_allclose(mi, mi_target)
assert ((set(dmda) - dmda_target[PGA()]) == set())
mi, dmda = gmice.getMIfromGM(amps_in + np.log(100),
PGV(),
dists=dists,
mag=None)
mi_target = np.array(
[5.11, 6.231235, 7.46, 8.16742, 8.581235, 8.874841, 9.10258])
np.testing.assert_allclose(mi, mi_target)
assert ((set(dmda) - dmda_target[PGV()]) == set())
mi, dmda = gmice.getMIfromGM(amps_in, SA(0.3), dists=None, mag=3.0)
mi_target = np.array([
3.68942245, 4.86791195, 6.15942245, 6.90296654, 7.33791195, 7.64651063,
7.88587836
])
np.testing.assert_allclose(mi, mi_target)
assert ((set(dmda) - dmda_target[SA(0.3)]) == set())
mi, dmda = gmice.getMIfromGM(amps_in, SA(1.0), dists=dists, mag=7.5)
mi_target = np.array(
[5.152921, 6.13102, 7.202921, 7.820033, 8.18102, 8.437144, 8.63581])
np.testing.assert_allclose(mi, mi_target)
assert ((set(dmda) - dmda_target[SA(1.0)]) == set())
mi, dmda = gmice.getMIfromGM(amps_in, SA(3.0), dists=None, mag=None)
mi_target = np.array(
[6.293338, 7.247581, 8.293338, 8.895398, 9.247581, 9.497458, 9.691278])
np.testing.assert_allclose(mi, mi_target)
assert ((set(dmda) - dmda_target[SA(3.0)]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, PGA(), dists=None, mag=7.0)
amps_target = np.array([
-6.60298051, -5.71050567, -4.81803083, -3.92555598, -2.85458617,
-1.87286385, -1.15888397
])
np.testing.assert_allclose(amps, amps_target)
assert ((set(dadm) - dadm_target[PGA()]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, PGV(), dists=dists, mag=None)
amps_target = np.array([
-3.04725091, -2.06742747, -1.08760402, -0.10778058, 1.06800755,
2.14581334, 2.9296721
])
np.testing.assert_allclose(amps, amps_target)
assert ((set(dadm) - dadm_target[PGV()]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, SA(0.3), dists=None, mag=6.0)
amps_target = np.array([
-6.18008474, -5.24786405, -4.31564337, -3.38342268, -2.26475786,
-1.23931511, -0.49353856
])
np.testing.assert_allclose(amps, amps_target)
assert ((set(dadm) - dadm_target[SA(0.3)]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, SA(1.0), dists=dists, mag=8.0)
amps_target = np.array([
-8.14657017, -7.02335793, -5.90014569, -4.77693345, -3.42907876,
-2.19354529, -1.2949755
])
assert ((set(dadm) - dadm_target[SA(1.0)]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, SA(3.0), dists=None, mag=5.0)
amps_target = np.array([
-9.54805824, -8.3967657, -7.24547315, -6.0941806, -4.71262955,
-3.44620775, -2.52517371
])
np.testing.assert_allclose(amps, amps_target)
np.testing.assert_allclose(amps, amps_target)
assert ((set(dadm) - dadm_target[SA(3.0)]) == set())
amps, dadm = gmice.getGMfromMI(mmi_in, PGA(), dists=None, mag=7.0)
amps_target = np.array([
-6.60298051, -5.71050567, -4.81803083, -3.92555598, -2.85458617,
-1.87286385, -1.15888397
])
np.testing.assert_allclose(amps, amps_target)
assert ((set(dadm) - dadm_target[PGA()]) == set())
sdd1 = gmice.getGM2MIsd()
assert sdd1[PGA()] == 0.18
assert sdd1[PGV()] == 0.14
assert sdd1[SA(0.3)] == 0.3
assert sdd1[SA(1.0)] == 0.21
assert sdd1[SA(3.0)] == 0.14
sdd2 = gmice.getMI2GMsd()
np.testing.assert_allclose(sdd2[PGA()], 0.7138013788281542)
np.testing.assert_allclose(sdd2[PGV()], 0.5065687204586901)
np.testing.assert_allclose(sdd2[SA(0.3)], 0.9670857390574993)
np.testing.assert_allclose(sdd2[SA(1.0)], 0.7138013788281542)
np.testing.assert_allclose(sdd2[SA(3.0)], 0.598672124178452)
nm = gmice.getName()
assert nm == 'Faenza and Michelini (2010, 2011)'
sc = gmice.getScale()
assert sc == 'scale_fm11.ps'
mm = gmice.getMinMax()
assert mm == (1.0, 10.0)
dt = gmice.getDistanceType()
assert dt == 'rrup'
#
# This should fail
#
with pytest.raises(ValueError): # noqa
mi, dmda = gmice.getMIfromGM(amps_in, MMI(), dists=None, mag=None)