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, '..', 'data',
'eventdata', '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 = read_rupture_file(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(ShakeMapException) as e:
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, '..', 'data',
'eventdata', '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 test_station(tmpdir):
homedir = os.path.dirname(os.path.abspath(__file__))
datadir = os.path.abspath(os.path.join(homedir, '..', 'data',
'eventdata', 'Calexico', 'input'))
#
# Read the event, source, and rupture files and produce a Source object
#
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')
source_obj = Source.fromFile(eventfile, rupturefile=rupturefile)
#
# Set up the GMPE, IPE, and GMICE
#
gmpe_cy14 = ChiouYoungs2014()
gmpe = MultiGMPE.from_list([gmpe_cy14], [1.0])
gmice = WGRW12()
ipe = AllenEtAl2012()
#
#
#
rupture_ctx = source_obj.getRuptureContext([gmpe])
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(
rupture_ctx.hypo_lon, rupture_ctx.hypo_lat, lonspan, latspan,
smdx, smdy, defaultVs30=760.0, vs30File=vs30filename,
vs30measured_grid=None, padding=False, resample=False
)
xmlfiles = [inputfile, dyfifile]
# dbfile = str(tmpdir.join('stations.db'))
dbfile = os.path.join(str(tmpdir), 'stations.db')
stations = StationList.fromXML(xmlfiles, dbfile, source_obj,
sites_obj_grid, gmpe, ipe, gmice)
df1 = stations.getStationDataframe(1, sort=True)
df2 = stations.getStationDataframe(0, sort=True)
#
# In case the test starts failing because of some minor change
# in one of the prediction or conversion equations (or roundoff
# or whatever), but the code is running correctly, uncomment
# these lines and re-run the test. Then, copy the new stations.db
# file into tests/data/eventdata/Calexico/database/. Then
# recomment these lines and rerun the test. It should succeed.
#
#shutil.copy(dbfile,'./stations.db')
#print(os.getcwd())
#
# We should probably check these dataframes against some established
# set, and also check the database against a known database.
#
ref_dbfile = os.path.join(datadir, '..', 'database', 'stations.db')
stations2 = StationList(ref_dbfile)
ref_df1 = stations2.getStationDataframe(1, sort=True)
ref_df2 = stations2.getStationDataframe(0, sort=True)
# assert ref_df1.equals(df1)
# assert ref_df2.equals(df2)
pdt.assert_frame_equal(df1, ref_df1)
pdt.assert_frame_equal(df2, ref_df2)
