def test(stationfile,xmlfile,eventdict):
tmp,dbfile = tempfile.mkstemp()
os.close(tmp)
os.remove(dbfile)
try:
print('Testing load from XML format...')
t1 = time.time()
stations1 = StationList.loadFromXML([xmlfile],dbfile)
t2 = time.time()
print('Passed load from XML format %i stations in %.2f seconds.' % (len(stations1),t2-t1))
print('Testing filling in distance and derived MMI/PGM values...')
source = Source(eventdict)
stations1.fillTables(source)
print('Passed filling in distance and derived MMI/PGM values...')
print('Testing retrieval of MMI data from StationList object...')
t1 = time.time()
mmidf1 = stations1.getMMIStations()
t2 = time.time()
print('Passed retrieval of %i MMI data in %.2f seconds from StationList object.' % (len(mmidf1),t2-t1))
print('Testing retrieval of instrumented data from StationList object...')
t1 = time.time()
imtdf1 = stations1.getInstrumentedStations()
t2 = time.time()
print('Passed retrieval of %i instrumented data in %.2f seconds from StationList object.' % (len(imtdf1),t2-t1))
print('Testing load from sqlite format...')
t1 = time.time()
stations2 = StationList(stationfile)
t2 = time.time()
print('Passed load from sqlite format %i stations in %.2f seconds.' % (len(stations1),t2-t1))
print('Testing retrieval of MMI data from StationList object...')
t1 = time.time()
mmidf2 = stations2.getMMIStations()
t2 = time.time()
print('Passed retrieval of %i MMI data in %.2f seconds from StationList object.' % (len(mmidf2),t2-t1))
print('Testing retrieval of instrumented data from StationList object...')
t1 = time.time()
imtdf2 = stations2.getInstrumentedStations()
t2 = time.time()
print('Passed retrieval of %i instrumented data in %.2f seconds from StationList object.' % (len(imtdf1),t2-t1))
assert(len(stations1) == len(stations2))
except Exception as msg:
print('Error caught: %s' % str(msg))
if os.path.isfile(dbfile):
os.remove(dbfile)
def test_so6():
event_name = 'so6'
magnitude = 7.2
dip = np.array([70])
rake = 135
width = np.array([15])
L = 80
fltx = np.array([0, 0])
flty = np.array([0, L])
zp = np.array([0])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(fltx, flty, reverse=True)
flt = fault.Fault.fromTrace(np.array([tlon[0]]),
np.array([tlat[0]]),
np.array([tlon[1]]),
np.array([tlat[1]]),
zp,
width,
dip,
reference='rv4')
x = np.linspace(-80, 80, 21)
y = np.linspace(-50, 130, 21)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
sdepth = np.zeros_like(slon)
tmp = flt.getQuadrilaterals()[0]
pp0 = Vector.fromPoint(
point.Point(tmp[0].longitude, tmp[0].latitude, tmp[0].depth))
pp1 = Vector.fromPoint(
point.Point(tmp[1].longitude, tmp[1].latitude, tmp[1].depth))
pp2 = Vector.fromPoint(
point.Point(tmp[2].longitude, tmp[2].latitude, tmp[2].depth))
pp3 = Vector.fromPoint(
point.Point(tmp[3].longitude, tmp[3].latitude, tmp[3].depth))
dxp = 10 / L
dyp = (width - 5) / width
mp0 = pp0 + (pp1 - pp0) * dxp
mp1 = pp3 + (pp2 - pp3) * dxp
rp = mp0 + (mp1 - mp0) * dyp
epilat, epilon, epidepth = ecef2latlon(rp.x, rp.y, rp.z)
epix, epiy = proj(epilon, epilat, reverse=False)
event = {
'lat': epilat,
'lon': epilon,
'depth': epidepth,
'mag': magnitude,
'id': 'so6',
'locstring': 'so6',
'type': 'RV',
'timezone': 'UTC'
}
event['time'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
event['created'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
fltlat = [a.latitude for a in flt.getQuadrilaterals()[0]]
fltlon = [a.longitude for a in flt.getQuadrilaterals()[0]]
fltlat = np.append(fltlat, fltlat[0])
fltlon = np.append(fltlon, fltlon[0])
fltx, flty = proj(fltlon, fltlat, reverse=False)
source = Source(event, flt)
source.setEventParam('rake', rake)
test1 = Bayless2013(source, slat, slon, sdepth, T=5)
fd = test1.getFd()
fd_test = np.array([
[
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -8.92879772e-03,
-1.74526918e-02, -2.22981746e-02, -2.34350450e-02, -2.13620062e-02,
-1.72712346e-02, -1.29509613e-02, -1.02545064e-02, -1.03010185e-02,
-1.28847597e-02, -1.66274727e-02, -1.96984070e-02, -2.05377743e-02,
-1.81831337e-02, -1.21881814e-02, -2.64862879e-03, 0.00000000e+00,
0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00, -8.73221519e-03, -2.21421374e-02,
-3.18438939e-02, -3.71488270e-02, -3.76239913e-02, -3.35015951e-02,
-2.61748968e-02, -1.83864728e-02, -1.34793002e-02, -1.36687799e-02,
-1.85727143e-02, -2.55527671e-02, -3.14227568e-02, -3.38933995e-02,
-3.19289607e-02, -2.53396980e-02, -1.45943649e-02, -3.71405488e-04,
0.00000000e+00
],
[
0.00000000e+00, -2.54621422e-03, -2.11428566e-02, -3.68609103e-02,
-4.87464747e-02, -5.56539037e-02, -5.64419387e-02, -5.05331157e-02,
-3.52919381e-02, -2.18782050e-02, -1.40858125e-02, -1.47354546e-02,
-2.35727189e-02, -3.74838465e-02, -4.75915414e-02, -5.13000399e-02,
-4.87882409e-02, -4.05716321e-02, -2.77368254e-02, -1.13542729e-02,
0.00000000e+00
],
[
0.00000000e+00, -1.21642958e-02, -3.33747360e-02, -5.21661817e-02,
-6.74724509e-02, -7.77628842e-02, -8.00243748e-02, -6.42496853e-02,
-4.38124530e-02, -1.97027426e-02, -1.45897731e-02, -1.07427056e-02,
-3.08235222e-02, -4.82656988e-02, -6.67692677e-02, -7.35152908e-02,
-6.85574283e-02, -5.71811573e-02, -4.12138780e-02, -2.20396726e-02,
-6.24121310e-04
],
[
0.00000000e+00, -2.00643401e-02, -4.39827328e-02, -6.62722434e-02,
-8.60268414e-02, -1.01730306e-01, -9.86277741e-02, -9.82914922e-02,
-5.22335876e-02, -1.54622435e-02, -1.57487554e-02, -3.06190808e-03,
-4.81481586e-02, -8.92480491e-02, -8.63776477e-02, -9.98130440e-02,
-8.95491230e-02, -7.33553695e-02, -5.34401725e-02, -3.11601812e-02,
-7.33715103e-03
],
[
0.00000000e+00, -2.50053614e-02, -5.11695772e-02, -7.65997026e-02,
-1.00809054e-01, -1.22877573e-01, -1.18738178e-01, -1.55236782e-01,
-7.45388001e-02, 1.92779182e-03, -1.94380016e-02, 1.94922939e-02,
-7.66669920e-02, -1.53909722e-01, -1.10846875e-01, -1.19746768e-01,
-1.07680300e-01, -8.59905101e-02, -6.22042294e-02, -3.71802472e-02,
-1.13867485e-02
],
[
0.00000000e+00, -2.63645827e-02, -5.37984901e-02, -8.11337022e-02,
-1.08298371e-01, -1.35146441e-01, -1.34825430e-01, -1.85836050e-01,
-1.10730875e-01, -3.18861095e-02, 4.14395701e-02, -1.52711946e-02,
-1.31840763e-01, -1.96794707e-01, -1.33453212e-01, -1.34989129e-01,
-1.17922385e-01, -9.21637323e-02, -6.58369237e-02, -3.91646838e-02,
-1.22685698e-02
],
[
0.00000000e+00, -2.64622244e-02, -5.40483999e-02, -8.16190336e-02,
-1.09162854e-01, -1.36656677e-01, -1.37081504e-01, -1.89522811e-01,
-1.17723634e-01, -4.88765748e-02, -5.04529015e-03, -5.76414497e-02,
-1.45712183e-01, -2.03062804e-01, -1.36859828e-01, -1.37107390e-01,
-1.19124650e-01, -9.28263279e-02, -6.61800709e-02, -3.93088682e-02,
-1.22842049e-02
],
[
0.00000000e+00, -2.58466495e-02, -5.24858827e-02, -7.86086164e-02,
-1.03856343e-01, -1.27529509e-01, -1.23794779e-01, -1.68810613e-01,
-8.22602627e-02, 1.74236964e-02, 9.38708725e-02, 4.23208284e-02,
-8.46343723e-02, -1.70476759e-01, -1.17547884e-01, -1.24569752e-01,
-1.11518670e-01, -8.84736806e-02, -6.38037151e-02, -3.81874381e-02,
-1.19867610e-02
],
[
0.00000000e+00, -2.42186547e-02, -4.84175525e-02, -7.09428614e-02,
-9.07754575e-02, -1.06117824e-01, -9.50228292e-02, -1.29781980e-01,
-3.08573454e-02, 7.39058739e-02, 1.30478117e-01, 8.28181149e-02,
-2.70389535e-02, -1.20837502e-01, -8.02081725e-02, -9.70274506e-02,
-9.35853383e-02, -7.77422806e-02, -5.77817530e-02, -3.53067886e-02,
-1.12414659e-02
],
[
0.00000000e+00, -2.16818717e-02, -4.22363856e-02, -5.96909893e-02,
-7.24805224e-02, -7.81867829e-02, -6.11838569e-02, -9.05679744e-02,
9.95934969e-03, 1.07503875e-01, 1.52073917e-01, 1.05894634e-01,
8.68652263e-03, -7.98571818e-02, -4.16548658e-02, -6.40511838e-02,
-6.99337160e-02, -6.26305633e-02, -4.89098800e-02, -3.09284566e-02,
-1.00919381e-02
],
[
0.00000000e+00, -1.84940182e-02, -3.47054606e-02, -4.65278129e-02,
-5.22037664e-02, -4.93977115e-02, -2.95395230e-02, -5.82421092e-02,
3.91025654e-02, 1.29337956e-01, 1.67436703e-01, 1.21969296e-01,
3.20823547e-02, -5.00287386e-02, -9.22993907e-03, -3.27186625e-02,
-4.52706958e-02, -4.57409325e-02, -3.84701291e-02, -2.55751405e-02,
-8.64950254e-03
],
[
0.00000000e+00, -1.49431380e-02, -2.65887341e-02, -3.29162158e-02,
-3.22994323e-02, -2.29081781e-02, -2.60259636e-03, -3.29856530e-02,
6.02631314e-02, 1.45003704e-01, 1.79361264e-01, 1.34292814e-01,
4.88007115e-02, -2.82328554e-02, 1.64212421e-02, -5.72391847e-03,
-2.23438861e-02, -2.90246794e-02, -2.76054402e-02, -1.97779758e-02,
-7.03945406e-03
],
[
0.00000000e+00, -1.12771143e-02, -1.84737590e-02, -1.98228664e-02,
-1.40092305e-02, 1.84580818e-04, 1.95817303e-02, -1.32608487e-02,
7.62783168e-02, 1.57076433e-01, 1.89083905e-01, 1.44259188e-01,
6.15722813e-02, -1.17505212e-02, 3.65938109e-02, 1.66937711e-02,
-2.18970818e-03, -1.35507683e-02, -1.70890527e-02, -1.39519424e-02,
-5.37036892e-03
],
[
0.00000000e+00, -7.67615215e-03, -1.07348257e-02, -7.75276739e-03,
2.22351695e-03, 1.98662250e-02, 3.77611177e-02, 2.42018661e-03,
8.89036172e-02, 1.66855206e-01, 1.97260700e-01, 1.52590263e-01,
7.17981256e-02, 1.18005972e-03, 5.26852303e-02, 3.51638855e-02,
1.51012176e-02, 2.69654076e-04, -7.33815554e-03, -8.36639665e-03,
-3.72176313e-03
],
[
0.00000000e+00, -4.50552324e-03, -4.32262850e-03, 1.73559158e-03,
1.42670366e-02, 3.35040699e-02, 4.97279358e-02, 1.85410528e-02,
9.39950666e-02, 1.46646579e-01, 9.13474746e-02, 1.37004651e-01,
7.74648339e-02, 1.59777072e-02, 6.25334939e-02, 4.74577418e-02,
2.72155518e-02, 1.06174952e-02, 3.94103899e-04, -3.68465400e-03,
-2.19830733e-03
],
[
0.00000000e+00, -1.74629916e-03, 5.44471813e-04, 8.22933499e-03,
2.15699287e-02, 4.04232250e-02, 5.69678048e-02, 5.52408259e-02,
9.04381272e-02, 1.08204635e-01, 9.14439984e-02, 1.06884511e-01,
8.17241884e-02, 5.55282924e-02, 6.78528399e-02, 5.47188925e-02,
3.35251483e-02, 1.69615982e-02, 5.72048628e-03, -8.81437278e-05,
-7.36518436e-04
],
[
0.00000000e+00, 4.07838765e-05, 3.63933766e-03, 1.20080876e-02,
2.51274691e-02, 4.25687176e-02, 6.25685606e-02, 7.33480475e-02,
8.37515545e-02, 9.52500287e-02, 9.15135660e-02, 9.66442834e-02,
8.66659913e-02, 8.10325633e-02, 7.18836713e-02, 5.45548434e-02,
3.55884875e-02, 2.00142359e-02, 8.71200201e-03, 2.04407846e-03,
-6.53680674e-06
],
[
0.00000000e+00, 2.40054729e-04, 4.44975227e-03, 1.27572519e-02,
2.49362989e-02, 4.03831326e-02, 5.80039988e-02, 7.61280192e-02,
8.37404162e-02, 8.89634569e-02, 9.15651607e-02, 9.13586235e-02,
8.83589144e-02, 8.27804032e-02, 6.75666471e-02, 5.00483249e-02,
3.36733366e-02, 1.96758691e-02, 9.00603204e-03, 2.18370401e-03,
0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00, 2.78776980e-03, 1.05086036e-02,
2.13238822e-02, 3.45577738e-02, 4.91570145e-02, 6.36787133e-02,
7.63710088e-02, 8.54072310e-02, 8.92960200e-02, 8.75702197e-02,
8.07095447e-02, 6.97999389e-02, 5.63787286e-02, 4.20734776e-02,
2.83073312e-02, 1.61614525e-02, 6.56194125e-03, 1.00721924e-04,
0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.49667845e-03,
1.47563319e-02, 2.57955743e-02, 3.76689418e-02, 4.91861917e-02,
5.90108907e-02, 6.58478416e-02, 6.87018515e-02, 6.73174642e-02,
6.20270643e-02, 5.35456385e-02, 4.29400416e-02, 3.14129728e-02,
2.00795162e-02, 9.84001885e-03, 1.53992995e-03, 0.00000000e+00,
0.00000000e+00
]
])
np.testing.assert_allclose(fd, fd_test, rtol=1e-4)
def test_rv4():
magnitude = 7.0
rake = 90.0
width = np.array([28])
fltx = np.array([0, 0])
flty = np.array([0, 32])
zp = np.array([0])
dip = np.array([30])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(fltx, flty, reverse=True)
flt = fault.Fault.fromTrace(np.array([tlon[0]]),
np.array([tlat[0]]),
np.array([tlon[1]]),
np.array([tlat[1]]),
zp,
width,
dip,
reference='')
L = flt.getFaultLength()
# Try to figure out epicenter
tmp = flt.getQuadrilaterals()[0]
pp0 = Vector.fromPoint(
point.Point(tmp[0].longitude, tmp[0].latitude, tmp[0].depth))
pp1 = Vector.fromPoint(
point.Point(tmp[1].longitude, tmp[1].latitude, tmp[1].depth))
pp2 = Vector.fromPoint(
point.Point(tmp[2].longitude, tmp[2].latitude, tmp[2].depth))
pp3 = Vector.fromPoint(
point.Point(tmp[3].longitude, tmp[3].latitude, tmp[3].depth))
dxp = 6 / L
dyp = (width - 8) / width
mp0 = pp0 + (pp1 - pp0) * dxp
mp1 = pp3 + (pp2 - pp3) * dxp
rp = mp0 + (mp1 - mp0) * dyp
epilat, epilon, epidepth = ecef2latlon(rp.x, rp.y, rp.z)
event = {
'lat': epilat,
'lon': epilon,
'depth': epidepth,
'mag': magnitude,
'id': 'test',
'locstring': 'rv4',
'type': 'DS',
'timezone': 'UTC'
}
event['time'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
event['created'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
x = np.linspace(-50, 50, 11)
y = np.linspace(-50, 50, 11)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
deps = np.zeros_like(slon)
source = Source(event, flt)
source.setEventParam('rake', rake)
test1 = Bayless2013(source, slat, slon, deps, T=2.0)
# Test fd
fd = test1.getFd()
fd_test = np.array(
[[
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.72143257e-03,
1.34977260e-03, 4.33616224e-15, 1.24446253e-03, 1.16142357e-03,
2.25464716e-03, 7.05281751e-04, 0.00000000e+00
],
[
0.00000000e+00, 0.00000000e+00, 7.62610242e-03, 1.25133844e-02,
5.61896104e-03, 7.63126014e-15, 4.52266194e-03, 4.67970900e-03,
1.02820316e-02, 5.13160096e-03, -6.13926251e-03
],
[
0.00000000e+00, 4.00495234e-03, 2.37608386e-02, 2.37139333e-02,
9.55224050e-03, 5.66364910e-15, 7.70344813e-03, 7.36466362e-03,
1.48239704e-02, 8.40388145e-03, -1.58592485e-02
],
[
8.08385547e-19, 9.38150101e-03, 3.38610620e-02, 3.85351492e-02,
1.91044918e-02, 3.98697802e-15, 1.54321666e-02, 1.21913760e-02,
2.04435166e-02, 1.04931859e-02, -1.85935894e-02
],
[
2.12025421e-18, 1.37316085e-02, 4.40193799e-02, 6.16562477e-02,
4.77612496e-02, 2.60257085e-15, 3.86322888e-02, 1.97965887e-02,
2.64882038e-02, 1.23335908e-02, -2.07389932e-02
],
[
2.64338576e-18, 1.45898292e-02, 4.89104213e-02, 7.70703166e-02,
9.55225258e-02, 1.01875104e-01, 7.73459329e-02, 2.50275508e-02,
2.93537540e-02, 1.30949577e-02, -2.15685454e-02
],
[
2.64330042e-18, 1.45898262e-02, 4.89104186e-02, 7.70703146e-02,
9.55225248e-02, 1.01910945e-01, 7.74050835e-02, 2.52307946e-02,
2.92970736e-02, 1.30880504e-02, -2.15685424e-02
],
[
2.64318867e-18, 1.45898259e-02, 4.89104184e-02, 7.70703144e-02,
9.55225247e-02, 1.01933432e-01, 7.74421258e-02, 2.53572923e-02,
2.92615130e-02, 1.30837284e-02, -2.15685422e-02
],
[
2.64305117e-18, 1.45898284e-02, 4.89104206e-02, 7.70703161e-02,
9.55225256e-02, 1.01942593e-01, 7.74571359e-02, 2.54081640e-02,
2.92472117e-02, 1.30819985e-02, -2.15685446e-02
],
[
2.30141673e-18, 1.40210825e-02, 4.56205547e-02, 6.63109661e-02,
5.79266964e-02, 2.33044622e-15, 4.69672564e-02, 2.18401553e-02,
2.72864925e-02, 1.25728575e-02, -2.10227772e-02
],
[
1.10672535e-18, 1.04777076e-02, 3.59041065e-02, 4.24614318e-02,
2.24217216e-02, 3.66914762e-15, 1.81728517e-02, 1.39301504e-02,
2.14956836e-02, 1.08711460e-02, -1.90802849e-02
]])
np.testing.assert_allclose(fd, fd_test, rtol=2e-4)
def test_ss3():
magnitude = 7.2
dip = np.array([90])
rake = 180.0
width = np.array([15])
fltx = np.array([0, 0])
flty = np.array([0, 80])
zp = np.array([0])
epix = np.array([0])
epiy = np.array([0.2 * flty[1]])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(fltx, flty, reverse=True)
epilon, epilat = proj(epix, epiy, reverse=True)
flt = fault.Fault.fromTrace(np.array([tlon[0]]),
np.array([tlat[0]]),
np.array([tlon[1]]),
np.array([tlat[1]]),
zp,
width,
dip,
reference='ss3')
event = {
'lat': epilat[0],
'lon': epilon[0],
'depth': 10,
'mag': magnitude,
'id': 'ss3',
'locstring': 'test',
'type': 'SS',
'timezone': 'UTC'
}
event['time'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
event['created'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
x = np.linspace(-60, 60, 21)
y = np.linspace(-60, 138, 34)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
deps = np.zeros_like(slon)
source = Source(event, flt)
source.setEventParam('rake', rake)
test1 = Bayless2013(source, slat, slon, deps, T=1.0)
# Test fd
fd = test1.getFd()
fd_test = np.array([
[
0.00000000e+00, 0.00000000e+00, 2.14620746e-03, 6.47899336e-03,
1.23119791e-02, 1.91676140e-02, 2.64009788e-02, 3.32427846e-02,
3.88863288e-02, 4.26104002e-02, 4.39120296e-02, 4.26104002e-02,
3.88863288e-02, 3.32427846e-02, 2.64009788e-02, 1.91676140e-02,
1.23119791e-02, 6.47899336e-03, 2.14620746e-03, 0.00000000e+00,
0.00000000e+00
],
[
0.00000000e+00, 8.57780996e-04, 3.99405791e-03, 9.31948105e-03,
1.65406113e-02, 2.51316805e-02, 3.43205435e-02, 4.31274592e-02,
5.04747209e-02, 5.53634169e-02, 5.70796092e-02, 5.53634169e-02,
5.04747209e-02, 4.31274592e-02, 3.43205435e-02, 2.51316805e-02,
1.65406113e-02, 9.31948105e-03, 3.99405791e-03, 8.57780996e-04,
0.00000000e+00
],
[
-7.32594549e-04, 1.80425497e-04, 3.76908220e-03, 1.00175179e-02,
1.86854835e-02, 2.92291145e-02, 4.07487277e-02, 5.20057177e-02,
6.15509770e-02, 6.79776087e-02, 7.02477931e-02, 6.79776087e-02,
6.15509770e-02, 5.20057177e-02, 4.07487277e-02, 2.92291145e-02,
1.86854835e-02, 1.00175179e-02, 3.76908220e-03, 1.80425497e-04,
-7.32594549e-04
],
[
-3.29238561e-03, -2.60643191e-03, 1.16635260e-03, 8.15185259e-03,
1.82290773e-02, 3.08983182e-02, 4.51608038e-02, 5.94769126e-02,
7.18919113e-02, 8.03888307e-02, 8.34165399e-02, 8.03888307e-02,
7.18919113e-02, 5.94769126e-02, 4.51608038e-02, 3.08983182e-02,
1.82290773e-02, 8.15185259e-03, 1.16635260e-03, -2.60643191e-03,
-3.29238561e-03
],
[
-7.68543266e-03, -7.63179286e-03, -4.08866637e-03, 3.27605236e-03,
1.45558215e-02, 2.94068040e-02, 4.68176355e-02, 6.49397159e-02,
7.72066272e-02, 8.50445368e-02, 8.77974692e-02, 8.50445368e-02,
7.72066272e-02, 6.49397159e-02, 4.68176355e-02, 2.94068040e-02,
1.45558215e-02, 3.27605236e-03, -4.08866637e-03, -7.63179286e-03,
-7.68543266e-03
],
[
-1.38078234e-02, -1.49011067e-02, -1.21731364e-02, -5.02168047e-03,
6.98177526e-03, 2.38268531e-02, 4.30419205e-02, 6.00041964e-02,
7.44541603e-02, 8.42939552e-02, 8.77989590e-02, 8.42939552e-02,
7.44541603e-02, 6.00041964e-02, 4.30419205e-02, 2.38268531e-02,
6.98177526e-03, -5.02168047e-03, -1.21731364e-02, -1.49011067e-02,
-1.38078234e-02
],
[
-2.13780396e-02, -2.42165379e-02, -2.30613142e-02, -1.70011475e-02,
-5.15036128e-03, 1.25885635e-02, 3.24536739e-02, 5.25619351e-02,
7.05100243e-02, 8.31900906e-02, 8.78003567e-02, 8.31900906e-02,
7.05100243e-02, 5.25619351e-02, 3.24536739e-02, 1.25885635e-02,
-5.15036128e-03, -1.70011475e-02, -2.30613142e-02, -2.42165379e-02,
-2.13780396e-02
],
[
-2.98882710e-02, -3.50862342e-02, -3.63793490e-02, -3.25716319e-02,
-2.22546618e-02, -3.59274163e-03, 1.83064517e-02, 4.20112440e-02,
6.46115966e-02, 8.14746164e-02, 8.78016623e-02, 8.14746164e-02,
6.46115966e-02, 4.20112440e-02, 1.83064517e-02, -3.59274163e-03,
-2.22546618e-02, -3.25716319e-02, -3.63793490e-02, -3.50862342e-02,
-2.98882710e-02
],
[
-3.85810679e-02, -4.66488633e-02, -5.12430987e-02, -5.10089462e-02,
-4.20856023e-02, -2.36905234e-02, -6.33876287e-04, 2.66765430e-02,
5.53289928e-02, 7.86066125e-02, 8.78028757e-02, 7.86066125e-02,
5.53289928e-02, 2.66765430e-02, -6.33876287e-04, -2.36905234e-02,
-4.20856023e-02, -5.10089462e-02, -5.12430987e-02, -4.66488633e-02,
-3.85810679e-02
],
[
-4.64803335e-02, -5.76615888e-02, -6.61458422e-02, -7.06512643e-02,
-6.38427394e-02, -4.77258398e-02, -2.55483969e-02, 4.05840724e-03,
3.98470070e-02, 7.33053399e-02, 8.78039969e-02, 7.33053399e-02,
3.98470070e-02, 4.05840724e-03, -2.55483969e-02, -4.77258398e-02,
-6.38427394e-02, -7.06512643e-02, -6.61458422e-02, -5.76615888e-02,
-4.64803335e-02
],
[
-5.25038299e-02, -6.66129442e-02, -7.90147081e-02, -8.87629178e-02,
-8.59653118e-02, -7.42828398e-02, -5.64316505e-02, -2.87083225e-02,
1.25945312e-02, 6.19971667e-02, 8.78050260e-02, 6.19971667e-02,
1.25945312e-02, -2.87083225e-02, -5.64316505e-02, -7.42828398e-02,
-8.59653118e-02, -8.87629178e-02, -7.90147081e-02, -6.66129442e-02,
-5.25038299e-02
],
[
-5.69779111e-02, -7.36791817e-02, -8.97495345e-02, -1.04799583e-01,
-1.07737239e-01, -1.02875880e-01, -9.46568471e-02, -7.95630162e-02,
-4.96285112e-02, 6.59954795e-03, 5.25569882e-02, 6.59954795e-03,
-4.96285112e-02, -7.95630162e-02, -9.46568471e-02, -1.02875880e-01,
-1.07737239e-01, -1.04799583e-01, -8.97495345e-02, -7.36791817e-02,
-5.69779111e-02
],
[
-5.90357675e-02, -7.69727119e-02, -9.48442826e-02, -1.12607620e-01,
-1.18744885e-01, -1.18201834e-01, -1.17217017e-01, -1.15152899e-01,
-1.09694433e-01, -8.82341332e-02, -1.61624035e-02, -8.82341332e-02,
-1.09694433e-01, -1.15152899e-01, -1.17217017e-01, -1.18201834e-01,
-1.18744885e-01, -1.12607620e-01, -9.48442826e-02, -7.69727119e-02,
-5.90357675e-02
],
[
-5.92189452e-02, -7.72680305e-02, -9.53051857e-02, -1.13322519e-01,
-1.19770917e-01, -1.19670660e-01, -1.19486798e-01, -1.19092639e-01,
-1.17989113e-01, -1.12555820e-01, -4.50009776e-02, -1.12555820e-01,
-1.17989113e-01, -1.19092639e-01, -1.19486798e-01, -1.19670660e-01,
-1.19770917e-01, -1.13322519e-01, -9.53051857e-02, -7.72680305e-02,
-5.92189452e-02
],
[
-5.79249958e-02, -7.51927112e-02, -9.20842554e-02, -1.08361430e-01,
-1.12722790e-01, -1.09732675e-01, -1.04531672e-01, -9.44729544e-02,
-7.23277773e-02, -2.05699911e-02, 3.58249631e-02, -2.05699911e-02,
-7.23277773e-02, -9.44729544e-02, -1.04531672e-01, -1.09732675e-01,
-1.12722790e-01, -1.08361430e-01, -9.20842554e-02, -7.51927112e-02,
-5.79249958e-02
],
[
-5.42527703e-02, -6.93641123e-02, -8.31684773e-02, -9.49114165e-02,
-9.41989454e-02, -8.48645354e-02, -7.00894708e-02, -4.58286259e-02,
-6.37563061e-03, 4.68887998e-02, 7.77968419e-02, 4.68887998e-02,
-6.37563061e-03, -4.58286259e-02, -7.00894708e-02, -8.48645354e-02,
-9.41989454e-02, -9.49114165e-02, -8.31684773e-02, -6.93641123e-02,
-5.42527703e-02
],
[
-4.82490057e-02, -5.99997941e-02, -6.91786120e-02, -7.44891242e-02,
-6.73705808e-02, -5.13001284e-02, -2.84188057e-02, 3.60143816e-03,
4.47470123e-02, 8.58663851e-02, 1.04548354e-01, 8.58663851e-02,
4.47470123e-02, 3.60143816e-03, -2.84188057e-02, -5.13001284e-02,
-6.73705808e-02, -7.44891242e-02, -6.91786120e-02, -5.99997941e-02,
-4.82490057e-02
],
[
-4.03203010e-02, -4.79063206e-02, -5.16352259e-02, -4.98707253e-02,
-3.67295509e-02, -1.57342058e-02, 1.13668830e-02, 4.46551184e-02,
8.10450840e-02, 1.11780747e-01, 1.24226598e-01, 1.11780747e-01,
8.10450840e-02, 4.46551184e-02, 1.13668830e-02, -1.57342058e-02,
-3.67295509e-02, -4.98707253e-02, -5.16352259e-02, -4.79063206e-02,
-4.03203010e-02
],
[
-3.10250239e-02, -3.40796094e-02, -3.22089254e-02, -2.37094100e-02,
-5.85463114e-03, 1.77402761e-02, 4.57786845e-02, 7.69637052e-02,
1.07537652e-01, 1.30906328e-01, 1.39800436e-01, 1.30906328e-01,
1.07537652e-01, 7.69637052e-02, 4.57786845e-02, 1.77402761e-02,
-5.85463114e-03, -2.37094100e-02, -3.22089254e-02, -3.40796094e-02,
-3.10250239e-02
],
[
-2.09301700e-02, -1.94475962e-02, -1.22970199e-02, 2.07296407e-03,
2.31516868e-02, 4.74574033e-02, 7.44743481e-02, 1.02380049e-01,
1.27776301e-01, 1.46003379e-01, 1.52690015e-01, 1.46003379e-01,
1.27776301e-01, 1.02380049e-01, 7.44743481e-02, 4.74574033e-02,
2.31516868e-02, 2.07296407e-03, -1.22970199e-02, -1.94475962e-02,
-2.09301700e-02
],
[
-1.05257992e-02, -4.74329696e-03, 7.12107274e-03, 2.63431361e-02,
4.93709790e-02, 7.31527220e-02, 9.82233938e-02, 1.22728059e-01,
1.43894925e-01, 1.58465026e-01, 1.63685984e-01, 1.58465026e-01,
1.43894925e-01, 1.22728059e-01, 9.82233938e-02, 7.31527220e-02,
4.93709790e-02, 2.63431361e-02, 7.12107274e-03, -4.74329696e-03,
-1.05257992e-02
],
[
-1.89098657e-04, 9.52392382e-03, 2.54577716e-02, 4.85730869e-02,
7.26048516e-02, 9.51726659e-02, 1.17988523e-01, 1.39380421e-01,
1.57176612e-01, 1.69076915e-01, 1.73274075e-01, 1.69076915e-01,
1.57176612e-01, 1.39380421e-01, 1.17988523e-01, 9.51726659e-02,
7.26048516e-02, 4.85730869e-02, 2.54577716e-02, 9.52392382e-03,
-1.89098657e-04
],
[
9.81732797e-03, 2.30419581e-02, 4.24234701e-02, 6.86213308e-02,
9.30164618e-02, 1.14050063e-01, 1.34620894e-01, 1.53304069e-01,
1.68420867e-01, 1.78321253e-01, 1.81774183e-01, 1.78321253e-01,
1.68420867e-01, 1.53304069e-01, 1.34620894e-01, 1.14050063e-01,
9.30164618e-02, 6.86213308e-02, 4.24234701e-02, 2.30419581e-02,
9.81732797e-03
],
[
1.93290725e-02, 3.56493099e-02, 5.79271157e-02, 8.65611122e-02,
1.10914315e-01, 1.30317702e-01, 1.48798006e-01, 1.65173224e-01,
1.78147031e-01, 1.86513895e-01, 1.89408199e-01, 1.86513895e-01,
1.78147031e-01, 1.65173224e-01, 1.48798006e-01, 1.30317702e-01,
1.10914315e-01, 8.65611122e-02, 5.79271157e-02, 3.56493099e-02,
1.93290725e-02
],
[
2.68168937e-02, 4.52356810e-02, 6.92261217e-02, 9.89630241e-02,
1.23093435e-01, 1.40640067e-01, 1.56998943e-01, 1.71215219e-01,
1.82297185e-01, 1.89360704e-01, 1.91789146e-01, 1.89360704e-01,
1.82297185e-01, 1.71215219e-01, 1.56998943e-01, 1.40640067e-01,
1.23093435e-01, 9.89630241e-02, 6.92261217e-02, 4.52356810e-02,
2.68168937e-02
],
[
3.19403269e-02, 5.15051953e-02, 7.61032066e-02, 1.05705197e-01,
1.31722206e-01, 1.47466588e-01, 1.61892450e-01, 1.74235616e-01,
1.83735386e-01, 1.89735533e-01, 1.91788616e-01, 1.89735533e-01,
1.83735386e-01, 1.74235616e-01, 1.61892450e-01, 1.47466588e-01,
1.31722206e-01, 1.05705197e-01, 7.61032066e-02, 5.15051953e-02,
3.19403269e-02
],
[
3.48604070e-02, 5.49292382e-02, 7.94274234e-02, 1.08149011e-01,
1.38923419e-01, 1.53070440e-01, 1.65849067e-01, 1.76646162e-01,
1.84871647e-01, 1.90029617e-01, 1.91787948e-01, 1.90029617e-01,
1.84871647e-01, 1.76646162e-01, 1.65849067e-01, 1.53070440e-01,
1.38923419e-01, 1.08149011e-01, 7.94274234e-02, 5.49292382e-02,
3.48604070e-02
],
[
3.53402022e-02, 5.53653759e-02, 7.91965502e-02, 1.06486934e-01,
1.36563003e-01, 1.57713955e-01, 1.69087164e-01, 1.78598269e-01,
1.85784340e-01, 1.90264452e-01, 1.91787141e-01, 1.90264452e-01,
1.85784340e-01, 1.78598269e-01, 1.69087164e-01, 1.57713955e-01,
1.36563003e-01, 1.06486934e-01, 7.91965502e-02, 5.53653759e-02,
3.53402022e-02
],
[
3.32889822e-02, 5.28319225e-02, 7.55769079e-02, 1.01077605e-01,
1.28592068e-01, 1.57023616e-01, 1.71766715e-01, 1.80199729e-01,
1.86528091e-01, 1.90454829e-01, 1.91786196e-01, 1.90454829e-01,
1.86528091e-01, 1.80199729e-01, 1.71766715e-01, 1.57023616e-01,
1.28592068e-01, 1.01077605e-01, 7.55769079e-02, 5.28319225e-02,
3.32889822e-02
],
[
2.87295370e-02, 4.74613283e-02, 6.88388861e-02, 9.23568989e-02,
1.17254645e-01, 1.42483223e-01, 1.66695764e-01, 1.81528776e-01,
1.87141877e-01, 1.90611190e-01, 1.91785112e-01, 1.90611190e-01,
1.87141877e-01, 1.81528776e-01, 1.66695764e-01, 1.42483223e-01,
1.17254645e-01, 9.23568989e-02, 6.88388861e-02, 4.74613283e-02,
2.87295370e-02
],
[
2.17650266e-02, 3.94568191e-02, 5.93023344e-02, 8.07720575e-02,
1.03124482e-01, 1.25394282e-01, 1.46405870e-01, 1.64828303e-01,
1.79288925e-01, 1.88553222e-01, 1.91747252e-01, 1.88553222e-01,
1.79288925e-01, 1.64828303e-01, 1.46405870e-01, 1.25394282e-01,
1.03124482e-01, 8.07720575e-02, 5.93023344e-02, 3.94568191e-02,
2.17650266e-02
],
[
1.25495284e-02, 2.90572166e-02, 4.72972116e-02, 6.67423656e-02,
8.66951873e-02, 1.06290296e-01, 1.24520131e-01, 1.40293247e-01,
1.52531693e-01, 1.60303860e-01, 1.62970689e-01, 1.60303860e-01,
1.52531693e-01, 1.40293247e-01, 1.24520131e-01, 1.06290296e-01,
8.66951873e-02, 6.67423656e-02, 4.72972116e-02, 2.90572166e-02,
1.25495284e-02
],
[
1.26441934e-03, 1.65114811e-02, 3.31390978e-02, 5.06407706e-02,
6.83765492e-02, 8.55839448e-02, 1.01408074e-01, 1.14955639e-01,
1.25373662e-01, 1.31946425e-01, 1.34193829e-01, 1.31946425e-01,
1.25373662e-01, 1.14955639e-01, 1.01408074e-01, 8.55839448e-02,
6.83765492e-02, 5.06407706e-02, 3.31390978e-02, 1.65114811e-02,
1.26441934e-03
],
[
0.00000000e+00, 2.06213867e-03, 1.71162845e-02, 3.27888240e-02,
4.85026462e-02, 6.35932476e-02, 7.73387997e-02, 8.90069217e-02,
9.79166934e-02, 1.03509489e-01, 1.05416736e-01, 1.03509489e-01,
9.79166934e-02, 8.90069217e-02, 7.73387997e-02, 6.35932476e-02,
4.85026462e-02, 3.27888240e-02, 1.71162845e-02, 2.06213867e-03,
0.00000000e+00
]
])
np.testing.assert_allclose(fd, fd_test, rtol=1e-4)
def _test_intensity():
datadir = os.path.abspath(
os.path.join(homedir, '..', 'data', 'eventdata', 'northridge'))
shakefile = os.path.join(datadir, 'northridge_grid.xml')
topofile = os.path.join(datadir, 'northridge_topo.grd')
faultfile = os.path.join(datadir, 'northridge_fault.txt')
cityfile = os.path.join(datadir, 'northridge_cities.txt')
coastfile = os.path.join(datadir, 'northridge_coastline.json')
countryfile = os.path.join(datadir, 'northridge_countries.json')
statefile = os.path.join(datadir, 'northridge_states.json')
lakefile = os.path.join(datadir, 'northridge_lakes.json')
oceanfile = os.path.join(datadir, 'northridge_ocean.json')
stationfile = os.path.join(datadir, 'northridge_stations.db')
roadfile = os.path.join(datadir, 'northridge_roads.json')
tancptfile = os.path.join(shakedir, 'shakemap', 'mapping', 'tan.cpt')
shakecptfile = os.path.join(shakedir, 'shakemap', 'mapping',
'shakecpt.cpt')
layerdict = {
'coast': coastfile,
'ocean': oceanfile,
'lake': lakefile,
'country': countryfile,
'roads': roadfile,
'state': statefile
}
tancolormap = GMTColorMap.loadFromCPT(tancptfile)
shakecolormap = GMTColorMap.loadFromCPT(shakecptfile)
cities = BasemapCities.loadFromCSV(cityfile)
shakemap = ShakeGrid.load(shakefile, adjust='res')
stations = StationList(stationfile)
fault = Fault.readFaultFile(faultfile)
edict = shakemap.getEventDict()
eventdict = {
'lat': edict['lat'],
'lon': edict['lon'],
'depth': edict['depth'],
'mag': edict['magnitude'],
'time': edict['event_timestamp']
}
source = Source(eventdict, fault)
maker = MapMaker(shakemap, topofile, stations, fault, layerdict, source,
cities)
# draw intensity map
outfolder = os.path.expanduser('~')
maker.setIntensityLayer('mmi')
maker.setIntensityGMTColorMap(shakecolormap)
intensity_map = maker.drawIntensityMap(outfolder)
print('Intensity map saved as: %s' % intensity_map)
# draw contour maps
maker.setContourGMTColorMap(tancolormap)
# Draw pgv contours
maker.setContourLayer('pgv')
contour_pgv_map = maker.drawContourMap(outfolder)
print('PGV contour map saved as: %s' % contour_pgv_map)
# Draw pga contours
maker.setContourLayer('pga')
contour_pga_map = maker.drawContourMap(outfolder)
print('PGA contour map saved as: %s' % contour_pga_map)
# Draw psa0.3 contours
maker.setContourLayer('psa03')
contour_psa03_map = maker.drawContourMap(outfolder)
print('PSA0.3 contour map saved as: %s' % contour_psa03_map)
# Draw psa1.0 contours
maker.setContourLayer('psa10')
contour_psa10_map = maker.drawContourMap(outfolder)
print('PSA1.0 contour map saved as: %s' % contour_psa10_map)
# Draw psa3.0 contours
maker.setContourLayer('psa30')
contour_psa30_map = maker.drawContourMap(outfolder)
print('PSA3.0 contour map saved as: %s' % contour_psa30_map)
def test_chichi():
print('Testing Chi-Chi...')
# read in fault file
f = '../data/0137A.POL'
i0 = np.arange(0, 9 * 11 * 3, 11)
i1 = i0 + 10
cs = zip(i0, i1)
df = pd.read_fwf(f, cs, skiprows=2, nrows=5, header=None)
mat = df.as_matrix()
ix = np.arange(0, 9 * 3, 3)
iy = ix + 1
iz = ix + 2
x0 = mat[0, ix]
x1 = mat[1, ix]
x2 = mat[2, ix]
x3 = mat[3, ix]
y0 = mat[0, iy]
y1 = mat[1, iy]
y2 = mat[2, iy]
y3 = mat[3, iy]
# Depth, positive down
z0 = np.abs(mat[0, iz])
z1 = np.abs(mat[1, iz])
z2 = np.abs(mat[2, iz])
z3 = np.abs(mat[3, iz])
epilat = 23.85
epilon = 120.82
proj = get_orthographic_projection(epilon - 1, epilon + 1, epilat + 1,
epilat - 1)
lon0, lat0 = proj(x0, y0, reverse=True)
lon1, lat1 = proj(x1, y1, reverse=True)
lon2, lat2 = proj(x2, y2, reverse=True)
lon3, lat3 = proj(x3, y3, reverse=True)
flt = Fault.fromVertices(lon0, lat0, z0, lon1, lat1, z1, lon2, lat2, z2,
lon3, lat3, z3)
ask14 = AbrahamsonEtAl2014()
# event information doesn't matter...
event = {
'lat': 0,
'lon': 0,
'depth': 0,
'mag': 7,
'id': '',
'locstring': '',
'type': 'U',
'time': ShakeDateTime.utcfromtimestamp(int(time.time())),
'timezone': 'UTC'
}
source = Source(event, flt)
# Get NGA distances
distfile = '../data/NGAW2_distances.csv'
df = pd.read_csv(distfile)
df2 = df.loc[df['EQID'] == 137]
slat = df2['Station Latitude'].as_matrix()
slon = df2['Station Longitude'].as_matrix()
sdep = np.zeros(slat.shape)
nga_repi = df2['EpiD (km)'].as_matrix()
nga_rhypo = df2['HypD (km)'].as_matrix()
nga_rrup = df2['ClstD (km)'].as_matrix()
nga_rjb = df2['Joyner-Boore Dist. (km)'].as_matrix()
nga_rx = df2['T'].as_matrix()
dist = Distance(ask14, source, slat, slon, sdep)
dctx = dist.getDistanceContext()
fig = plt.figure(figsize=(8, 8))
plt.scatter(nga_rjb, dctx.rjb, alpha=0.5, facecolors='none')
plt.plot([0, nga_rjb.max()], [0, dctx.rjb.max()], 'b')
plt.savefig('Chi-Chi_Rjb.png')
fig = plt.figure(figsize=(8, 8))
plt.scatter(nga_rrup, dctx.rrup, alpha=0.5, facecolors='none')
plt.plot([0, nga_rrup.max()], [0, dctx.rrup.max()], 'b')
plt.savefig('Chi-Chi_Rrup.png')
fig = plt.figure(figsize=(8, 8))
plt.scatter(nga_rx, dctx.rx, alpha=0.5, facecolors='none')
plt.plot([nga_rx.min(), nga_rx.max()],
[dctx.rx.min(), dctx.rx.max()], 'b')
plt.savefig('Chi-Chi_Rx.png')
def _test():
tmp, dbfile = tempfile.mkstemp()
os.close(tmp)
os.remove(dbfile)
homedir = os.path.dirname(os.path.abspath(__file__))
xmlfile = os.path.abspath(
os.path.join(homedir, '..', 'data', 'eventdata', 'northridge',
'northridge_stations.xml'))
stationfile = os.path.abspath(
os.path.join(homedir, '..', 'data', 'eventdata', 'northridge',
'northridge_stations.db'))
eventdict = {
'lat': 34.213,
'lon': -118.537,
'depth': 18.2,
'mag': 6.7,
'time': datetime(1994, 1, 17, 12, 30, 55),
'mech': 'ALL',
'dip': 45,
'rake': 90
}
try:
print('Testing load from XML format...')
t1 = time.time()
stations1 = StationList.loadFromXML([xmlfile], dbfile)
t2 = time.time()
print('Passed load from XML format %i stations in %.2f seconds.' %
(len(stations1), t2 - t1))
print('Testing filling in distance and derived MMI/PGM values...')
source = Source(eventdict)
stations1.fillTables(source)
print('Passed filling in distance and derived MMI/PGM values...')
print('Testing retrieval of MMI data from StationList object...')
t1 = time.time()
mmidf1 = stations1.getMMIStations()
t2 = time.time()
print(
'Passed retrieval of %i MMI data in %.2f seconds from StationList object.'
% (len(mmidf1), t2 - t1))
print(
'Testing retrieval of instrumented data from StationList object...'
)
t1 = time.time()
imtdf1 = stations1.getInstrumentedStations()
t2 = time.time()
print(
'Passed retrieval of %i instrumented data in %.2f seconds from StationList object.'
% (len(imtdf1), t2 - t1))
print('Testing load from sqlite format...')
t1 = time.time()
stations2 = StationList(stationfile)
t2 = time.time()
print('Passed load from sqlite format %i stations in %.2f seconds.' %
(len(stations1), t2 - t1))
print('Testing retrieval of MMI data from StationList object...')
t1 = time.time()
mmidf2 = stations2.getMMIStations()
t2 = time.time()
print(
'Passed retrieval of %i MMI data in %.2f seconds from StationList object.'
% (len(mmidf2), t2 - t1))
print(
'Testing retrieval of instrumented data from StationList object...'
)
t1 = time.time()
imtdf2 = stations2.getInstrumentedStations()
t2 = time.time()
print(
'Passed retrieval of %i instrumented data in %.2f seconds from StationList object.'
% (len(imtdf1), t2 - t1))
assert (len(stations1) == len(stations2))
except Exception as msg:
print('Error caught: %s' % str(msg))
if os.path.isfile(dbfile):
os.remove(dbfile)
