def test_ss3_m4p5():
magnitude = 4.5
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 = rupture.QuadRupture.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(0, 20, 6)
y = np.linspace(0, 90, 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=1.0)
# Test fd
fd = test1.getFd()
fd_test = np.array(
[[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0.]])
np.testing.assert_allclose(
fd, fd_test, rtol=1e-4)
def test_exceptions():
vs30file = os.path.join(shakedir, 'tests/data/Vs30_test.grd')
cx = -118.2
cy = 34.1
dx = 0.0083
dy = 0.0083
xspan = 0.0083 * 5
yspan = 0.0083 * 5
site = Sites.fromCenter(cx, cy, xspan, yspan, dx, dy,
vs30File=vs30file,
padding=True, resample=False)
# Make souce instance
lat0 = np.array([34.1])
lon0 = np.array([-118.2])
lat1 = np.array([34.2])
lon1 = np.array([-118.15])
z = np.array([1.0])
W = np.array([3.0])
dip = np.array([30.])
flt = Fault.fromTrace(lon0, lat0, lon1, lat1, z, W, dip)
event = {'lat': 34.1, 'lon': -118.2, 'depth': 1, 'mag': 6,
'id': '', 'locstring': '', 'type': 'U', 'mech':'RS',
'rake':90,
'time': ShakeDateTime.utcfromtimestamp(int(time.time())),
'timezone': 'UTC'}
source = Source(event, flt)
gmpelist = ["Primate"]
with pytest.raises(Exception) as e:
dists = Distance.fromSites(gmpelist, source, site)
gmpelist = [AbrahamsonEtAl2014()]
sctx = site.getSitesContext()
dist_types = ['repi', 'rhypo', 'rjb', 'rrup', 'rx', 'ry', 'ry0', 'U', 'V']
with pytest.raises(Exception) as e:
dists = get_distance(dist_types, sctx.lats, sctx.lons,
np.zeros_like(sctx.lons), source)
dist_types = ['repi', 'rhypo', 'rjb', 'rrup', 'rx', 'ry', 'ry0', 'U', 'T']
with pytest.raises(Exception) as e:
dists = get_distance(dist_types, sctx.lats, sctx.lons[0:4,],
np.zeros_like(sctx.lons), source)
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,
1.72147747e-03, 1.34981119e-03, 8.95673480e-29,
1.24449087e-03, 1.16145147e-03, 2.25470229e-03,
7.05301515e-04, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 7.62625126e-03,
1.25136528e-02, 5.61909403e-03, 3.18694606e-28,
4.52275052e-03, 4.67980272e-03, 1.02822365e-02,
5.13171639e-03, -6.13935060e-03],
[ 0.00000000e+00, 4.00499692e-03, 2.37611880e-02,
2.37143264e-02, 9.55241972e-03, 5.65693221e-28,
7.70357238e-03, 7.36477919e-03, 1.48241947e-02,
8.40402367e-03, -1.58594139e-02],
[ 8.08392720e-19, 9.38156493e-03, 3.38613859e-02,
3.85355818e-02, 1.91047521e-02, 1.27066310e-27,
1.54323543e-02, 1.21915074e-02, 2.04437211e-02,
1.04933053e-02, -1.85937074e-02],
[ 2.12026318e-18, 1.37316424e-02, 4.40195705e-02,
6.16565712e-02, 4.77616016e-02, 5.07336347e-27,
3.86325509e-02, 1.97966900e-02, 2.64883302e-02,
1.23336661e-02, -2.07390404e-02],
[ 2.64338576e-18, 1.45898071e-02, 4.89104103e-02,
7.70703129e-02, 9.55225254e-02, 1.01875104e-01,
7.73459333e-02, 2.50275520e-02, 2.93537605e-02,
1.30949772e-02, -2.15685118e-02],
[ 2.64330042e-18, 1.45898071e-02, 4.89104103e-02,
7.70703129e-02, 9.55225254e-02, 1.01910945e-01,
7.74050830e-02, 2.52307951e-02, 2.92970785e-02,
1.30880672e-02, -2.15685118e-02],
[ 2.64318867e-18, 1.45898071e-02, 4.89104103e-02,
7.70703129e-02, 9.55225254e-02, 1.01933432e-01,
7.74421253e-02, 2.53572928e-02, 2.92615177e-02,
1.30837449e-02, -2.15685118e-02],
[ 2.64305117e-18, 1.45898071e-02, 4.89104103e-02,
7.70703129e-02, 9.55225254e-02, 1.01942593e-01,
7.74571361e-02, 2.54081650e-02, 2.92472178e-02,
1.30820173e-02, -2.15685118e-02],
[ 2.30140686e-18, 1.40209885e-02, 4.56202616e-02,
6.63103459e-02, 5.79255225e-02, 7.72925496e-27,
4.69663059e-02, 2.18399567e-02, 2.72863359e-02,
1.25728195e-02, -2.10226512e-02],
[ 1.10671369e-18, 1.04775558e-02, 3.59035524e-02,
4.24605614e-02, 2.24210618e-02, 1.53459722e-27,
1.81723013e-02, 1.39298662e-02, 2.14953705e-02,
1.08710398e-02, -1.90800441e-02]]
)
np.testing.assert_allclose(fd, fd_test, rtol=1e-5)
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 __init__(self, event):
"""
Construct an Origin object.
Args:
event (dict): Dictionary of values. See list above for required keys.
Returns:
Origin object.
"""
#-----------------------------------------------------------------------
# Check for missing keys
#-----------------------------------------------------------------------
missing = []
for req in REQUIRED_KEYS:
if req not in list(event.keys()):
missing.append(req)
if len(missing):
raise Exception('Input event dictionary is missing the following '
'required keys: "%s"' % (','.join(missing)))
#-----------------------------------------------------------------------
# Check some types, ranges, and defaults
#-----------------------------------------------------------------------
if not type(event['id']) is str:
raise Exception('id must be a string.')
event['lat'] = float(event['lat'])
if (event['lat'] > 90) or (event['lat'] < -90):
raise Exception('lat must be between -90 and 90 degrees.')
event['lon'] = float(event['lon'])
if (event['lon'] > 180) or (event['lon'] < -180):
raise Exception('lat must be between -180 and 180 degrees.')
event['depth'] = float(event['depth'])
event['mag'] = float(event['mag'])
if 'mech' in event.keys():
if event['mech'] == '':
event['mech'] = DEFAULT_MECH
if not event['mech'] in RAKEDICT.keys():
raise Exception('mech must be SS, NM, RS, or ALL.')
elif 'type' in event.keys():
event['mech'] = event['type']
if event['mech'] == '':
event['mech'] = DEFAULT_MECH
if not event['mech'] in RAKEDICT.keys():
raise Exception('mech must be SS, NM, RS, or ALL.')
else:
event['mech'] = DEFAULT_MECH
#-----------------------------------------------------------------------
# Special handling of time
#-----------------------------------------------------------------------
if ('year' in event.keys()) and \
('month' in event.keys()) and \
('day' in event.keys()) and \
('hour' in event.keys()) and \
('second' in event.keys()) and \
('msec' in event.keys()):
year = int(event['year'])
month = int(event['month'])
day = int(event['day'])
hour = int(event['hour'])
minute = int(event['minute'])
second = float(event['second'])
msec = int((second - int(second)) * 1e6) # microsec
second = int(second)
event['time'] = ShakeDateTime(
year, month, day, hour, minute, second, msec)
else:
event['time'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
#-----------------------------------------------------------------------
# Add keys as class attributes
#-----------------------------------------------------------------------
for k, v in event.items():
if k == 'rake':
setattr(self, k, float(v))
else:
setattr(self, k, v)
# What about rake?
if not hasattr(self, 'rake'):
if hasattr(self, 'mech'):
mech = self.mech
self.rake = RAKEDICT[mech]
else:
self.rake = RAKEDICT['ALL']
def test_multigmpe():
# Define gmpes and their weights
gmpes = [AbrahamsonEtAl2014(), BooreEtAl2014(),
CampbellBozorgnia2014(), ChiouYoungs2014()]
wts = [0.25, 0.25, 0.25, 0.25]
# Make sites instance
vs30file = os.path.join(shakedir, 'tests/data/Vs30_test.grd')
cx = -118.2
cy = 34.1
dx = 0.0083
dy = 0.0083
xspan = 0.0083 * 5
yspan = 0.0083 * 5
site = Sites.createFromCenter(cx, cy, xspan, yspan, dx, dy,
vs30File=vs30file,
padding=True, resample=False)
sctx = site.getSitesContext()
sctx.vs30 = np.reshape(sctx.vs30, (-1,))
sctx.vs30measured = np.reshape(sctx.vs30measured, (-1,))
sctx.z1pt0 = np.reshape(sctx.z1pt0, (-1,))
# Need separate z1pt0 arrays
sctx.z1pt0cy14 = mg._z1_from_vs30_cy14_cal(sctx.vs30)
sctx.z1pt0ask14 = mg._z1_from_vs30_ask14_cal(sctx.vs30)
sctx.z2pt5 = mg._z2p5_from_vs30_cb14_cal(sctx.vs30) / 1000.0
# Make souce instance
lat0 = np.array([34.1])
lon0 = np.array([-118.2])
lat1 = np.array([34.2])
lon1 = np.array([-118.15])
z = np.array([1.0])
W = np.array([3.0])
dip = np.array([30.])
flt = Fault.fromTrace(lon0, lat0, lon1, lat1, z, W, dip)
event = {'lat': 34.1, 'lon': -118.2, 'depth': 1, 'mag': 6,
'id': '', 'locstring': '', 'rake': 30.3,
'time': ShakeDateTime.utcfromtimestamp(int(time.time())),
'timezone': 'UTC'}
source = Source(event, flt)
# Make a rupture context
rupt = source.getRuptureContext(gmpes)
# Make a distance context
dctx = Distance.fromSites(gmpes, source, site).getDistanceContext()
dctx.rhypo = np.reshape(dctx.rhypo, (-1,))
dctx.rx = np.reshape(dctx.rx, (-1,))
dctx.rjb = np.reshape(dctx.rjb, (-1,))
dctx.ry0 = np.reshape(dctx.ry0, (-1,))
dctx.rrup = np.reshape(dctx.rrup, (-1,))
# Compute weighted GMPE
iimt = imt.PGV()
stddev_types = [const.StdDev.TOTAL]
mgmpe = mg.MultiGMPE.from_list(gmpes, wts)
lnmu, lnsd = mgmpe.get_mean_and_stddevs(
sctx, rupt, dctx, iimt, stddev_types)
lnmud = np.array(
[3.44828531, 3.49829605, 3.61749432, 3.64343805, 3.7001028,
3.7348924, 3.76927164, 3.78659955, 3.82600784, 3.46635007,
3.53816879, 3.6486898, 3.67058155, 3.72223342, 3.75403094,
3.79315031, 3.79871491, 3.82093027, 3.54889613, 3.57531437,
3.64441687, 3.69915981, 3.74491289, 3.78931599, 3.80957828,
3.80870754, 3.8731021, 3.5927326, 3.60764647, 3.66894024,
3.72148551, 3.75742965, 3.82164661, 3.86341308, 3.87171115,
3.79092594, 3.64153758, 3.61835381, 3.68166249, 3.7338161,
3.82454214, 3.81543928, 3.81507658, 3.80006803, 3.77165695,
3.65178742, 3.71324776, 3.70389969, 3.77034752, 3.78259432,
3.78677497, 3.79838465, 3.79050287, 3.75066018, 3.52883328,
3.67813977, 3.71754876, 3.65520574, 3.69463436, 3.72516445,
3.7457098, 3.74672185, 3.72615784, 3.44535551, 3.61907294,
3.58790363, 3.58068716, 3.61177983, 3.64349327, 3.66698468,
3.67129902, 3.65483002]
)
lnsdd = np.array(
[ 0.63560302, 0.63648101, 0.63610581, 0.6390135 , 0.64203528,
0.64624098, 0.64851812, 0.64640406, 0.64384305, 0.6361429 ,
0.63677975, 0.63715381, 0.64040366, 0.64404005, 0.64782624,
0.6476325 , 0.64509458, 0.64297808, 0.63477576, 0.63727968,
0.63899462, 0.64205578, 0.64604037, 0.64815296, 0.64609948,
0.64402734, 0.63844724, 0.6343891 , 0.63806041, 0.64043609,
0.64406094, 0.64776777, 0.64717195, 0.64297191, 0.64011346,
0.64110084, 0.63137566, 0.63864151, 0.64163093, 0.64588687,
0.64714873, 0.64603694, 0.64397734, 0.64217431, 0.63958323,
0.62883338, 0.63127469, 0.63961477, 0.64097303, 0.6442055 ,
0.64376449, 0.64273526, 0.64112115, 0.63815862, 0.63575399,
0.6291859 , 0.63180644, 0.6394421 , 0.63946545, 0.63947169,
0.63935499, 0.63832598, 0.63664816, 0.63595663, 0.62755689,
0.63523274, 0.63663489, 0.63631586, 0.63616589, 0.63597828,
0.63542126, 0.63500847])
np.testing.assert_allclose(lnmu, lnmud)
np.testing.assert_allclose(lnsd[0], lnsdd)
# Check for exception due to weights:
with pytest.raises(Exception) as a:
wts = [0.25, 0.25, 0.25, 0.25 + 1e-4]
mgmpe = mg.MultiGMPE.from_list(gmpes, wts)
# Check exception on GMPE check
with pytest.raises(Exception) as a:
wts = [1.0]
mgmpe = mg.MultiGMPE.from_list(['BA08'], wts)
# Check exception on tectonic region
with pytest.raises(Exception) as a:
gmpes = [BooreEtAl2014(), Campbell2003()]
wts = [0.5, 0.5]
mgmpe = mg.MultiGMPE.from_list(gmpes, wts)
# Check exception on length of gmpe and weight lenghts
with pytest.raises(Exception) as a:
gmpes = [BooreEtAl2014(), Campbell2003()]
wts = [1.0]
mgmpe = mg.MultiGMPE.from_list(gmpes, wts)
# Check PGV from a GMPE without PGV
gmpes = [Campbell2003()]
wts = [1.0]
mgmpe = mg.MultiGMPE.from_list(gmpes, wts)
lnmu, lnsd = mgmpe.get_mean_and_stddevs(
sctx, rupt, dctx, iimt, stddev_types)
lnmud = np.array(
[ 3.09152212, 3.1524312 , 3.20749883, 3.25431585, 3.29035521,
3.31326677, 3.32116911, 3.31341321, 3.29819842, 3.12252648,
3.18081138, 3.23208034, 3.27383205, 3.30358765, 3.319195 ,
3.31916753, 3.30623521, 3.28938984, 3.15235911, 3.20745205,
3.25429394, 3.29035582, 3.31328548, 3.32119931, 3.31344697,
3.2982328 , 3.27982759, 3.17945026, 3.23203088, 3.2738231 ,
3.30360265, 3.31922869, 3.31921198, 3.30628471, 3.28944133,
3.26955097, 3.18990634, 3.24351181, 3.28521502, 3.31195497,
3.32124956, 3.3135073 , 3.29830033, 3.27989827, 3.25860053,
3.17942778, 3.23201703, 3.27282524, 3.29888607, 3.3078892 ,
3.30156745, 3.2884687 , 3.26964276, 3.24701758, 3.14910673,
3.19888101, 3.23727522, 3.26163304, 3.2701699 , 3.2690822 ,
3.26201491, 3.24919602, 3.23101321, 3.10184816, 3.1475792 ,
3.18259748, 3.20467529, 3.21444387, 3.21832088, 3.21671138,
3.20966263, 3.19737325]
)
lnsdd = np.array(
[ 0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518, 0.83458518, 0.83458518, 0.83458518,
0.83458518, 0.83458518]
)
np.testing.assert_allclose(lnmu, lnmud)
np.testing.assert_allclose(lnsd[0], lnsdd)
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_distance_no_fault():
# Make sites instance
vs30file = os.path.join(shakedir, 'tests/data/Vs30_test.grd')
cx = -118.2
cy = 34.1
dx = 0.0083
dy = 0.0083
xspan = 0.0083 * 5
yspan = 0.0083 * 5
site = Sites.createFromCenter(cx, cy, xspan, yspan, dx, dy,
vs30File=vs30file,
padding=True, resample=False)
# Make souce instance
# - Unknown/no tectonic region
# - Mech is ALL
event = {'lat': 34.1, 'lon': -118.2, 'depth': 1, 'mag': 6,
'id': '', 'locstring': '',
'time': ShakeDateTime.utcfromtimestamp(int(time.time())),
'timezone': 'UTC'}
source = Source(event)
source.setMechanism('ALL')
gmpe = AbrahamsonEtAl2014()
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[1.69885399, 1.43717125, 1.2401229, 1.12306082, 1.08344161,
1.12306082, 1.2401229, 1.43717125, 1.69885399],
[1.31170897, 1.03475736, 0.82507289, 0.69891123, 0.6553167,
0.69891123, 0.82507289, 1.03475736, 1.31170897],
[1.02288894, 0.72918893, 0.50725874, 0.36675284, 0.31957002,
0.36675284, 0.50725874, 0.72918893, 1.02288894],
[0.83950888, 0.53487557, 0.30135487, 0.14776055, 0.09290799,
0.14776055, 0.30135487, 0.53487557, 0.83950888],
[0.77835253, 0.46963915, 0.2280053, 0.06643829, 0.,
0.06643829, 0.2280053, 0.46963915, 0.77835253],
[0.83964536, 0.53495323, 0.30139344, 0.14777086, 0.09290799,
0.14777086, 0.30139344, 0.53495323, 0.83964536],
[1.02314515, 0.72933979, 0.50732902, 0.36677229, 0.31957002,
0.36677229, 0.50732902, 0.72933979, 1.02314515],
[1.31208408, 1.03497272, 0.82517579, 0.69893672, 0.6553167,
0.69893672, 0.82517579, 1.03497272, 1.31208408]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[8.55244227, 8.44386675, 8.30721612, 8.28522381, 8.27615555,
8.28522381, 8.30721612, 8.44386675, 8.55244227],
[8.36298714, 8.26004043, 8.11921658, 8.11376473, 8.09579991,
8.11376473, 8.11921658, 8.26004043, 8.36298714],
[8.25524589, 8.12023764, 8.01816823, 7.93517669, 7.9600631,
7.93517669, 8.01816823, 8.12023764, 8.25524589],
[8.13306689, 8.00702434, 7.95888951, 7.8881565, 7.88352525,
7.8881565, 7.95888951, 8.00702434, 8.13306689],
[8.12102175, 8.02022568, 7.90722113, 7.85639147, 0.,
7.85639147, 7.90722113, 8.02022568, 8.12102175],
[8.13319461, 8.00698213, 7.95890141, 7.88813785, 7.88352525,
7.88813785, 7.95890141, 8.00698213, 8.13319461],
[8.25535307, 8.12025813, 8.01814988, 7.93520725, 7.9600631,
7.93520725, 8.01814988, 8.12025813, 8.25535307],
[8.36326021, 8.26012423, 8.11931772, 8.1137722, 8.09579991,
8.1137722, 8.11931772, 8.26012423, 8.36326021]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
# Souce instance
# - Tectonic region: active
# - Mech is ALL
source.setMechanism('ALL')
source._tectonic_region = 'Active Shallow Crust'
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[1.7826962, 1.5154877, 1.31394288, 1.19278311, 1.15168205,
1.19278311, 1.31394288, 1.5154877, 1.7826962],
[1.38719993, 1.10115976, 0.88331216, 0.75077594, 0.70491937,
0.75077594, 0.88331216, 1.10115976, 1.38719993],
[1.08884025, 0.78261273, 0.54865189, 0.39914713, 0.34852149,
0.39914713, 0.54865189, 0.78261273, 1.08884025],
[0.8983245, 0.57800579, 0.32895654, 0.16294131, 0.10290794,
0.16294131, 0.32895654, 0.57800579, 0.8983245],
[0.83428851, 0.50865092, 0.25000954, 0.07371315, 0.,
0.07371315, 0.25000954, 0.50865092, 0.83428851],
[0.89846642, 0.57808833, 0.32899797, 0.16295256, 0.10290794,
0.16295256, 0.32899797, 0.57808833, 0.89846642],
[1.08910622, 0.78277133, 0.5487266, 0.39916785, 0.34852149,
0.39916785, 0.5487266, 0.78277133, 1.08910622],
[1.38758368, 1.10138329, 0.88341918, 0.75080275, 0.70491937,
0.75080275, 0.88341918, 1.10138329, 1.38758368]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[9.0098969, 8.90622877, 8.76985073, 8.7508322, 8.74262729,
8.7508322, 8.76985073, 8.90622877, 9.0098969],
[8.82590475, 8.72726953, 8.58534811, 8.58323541, 8.5654409,
8.58323541, 8.58534811, 8.72726953, 8.82590475],
[8.72260676, 8.58922774, 8.4871601, 8.40244887, 8.4297141,
8.40244887, 8.4871601, 8.58922774, 8.72260676],
[8.59952763, 8.47470803, 8.42880892, 8.35631312, 8.35201539,
8.35631312, 8.42880892, 8.47470803, 8.59952763],
[8.58864463, 8.49021276, 8.37536496, 8.32350188, 0.,
8.32350188, 8.37536496, 8.49021276, 8.58864463],
[8.59965826, 8.47466149, 8.42882081, 8.35629324, 8.35201539,
8.35629324, 8.42882081, 8.47466149, 8.59965826],
[8.72271133, 8.58924512, 8.48713903, 8.4024808, 8.4297141,
8.4024808, 8.48713903, 8.58924512, 8.72271133],
[8.8261781, 8.72735075, 8.58545174, 8.5832426, 8.5654409,
8.5832426, 8.58545174, 8.72735075, 8.8261781]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
# Souce instance
# - Tectonic region: active
# - Mech is RS
source.setMechanism('RS')
source._tectonic_region = 'Active Shallow Crust'
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[1.62698377, 1.35570722, 1.15237308, 1.03338675, 0.99327828,
1.03338675, 1.15237308, 1.35570722, 1.62698377],
[1.22613799, 0.94405696, 0.73298036, 0.61025288, 0.56806407,
0.61025288, 0.73298036, 0.94405696, 1.22613799],
[0.93206877, 0.63960501, 0.42680989, 0.29738908, 0.25562246,
0.29738908, 0.42680989, 0.63960501, 0.93206877],
[0.74745861, 0.45239173, 0.23958848, 0.10874248, 0.06508855,
0.10874248, 0.23958848, 0.45239173, 0.74745861],
[0.68734787, 0.39203111, 0.17571632, 0.04480241, 0.,
0.04480241, 0.17571632, 0.39203111, 0.68734787],
[0.74759552, 0.45246373, 0.2396224, 0.1087508, 0.06508855,
0.1087508, 0.2396224, 0.45246373, 0.74759552],
[0.93232753, 0.63975135, 0.42687494, 0.29740691, 0.25562246,
0.29740691, 0.42687494, 0.63975135, 0.93232753],
[1.22652484, 0.94427453, 0.73308353, 0.61027758, 0.56806407,
0.61027758, 0.73308353, 0.94427453, 1.22652484]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[9.10924586, 9.00129817, 8.86119702, 8.8412603, 8.83276567,
8.8412603, 8.86119702, 9.00129817, 9.10924586],
[8.91865949, 8.81700098, 8.67259556, 8.6707467, 8.65288076,
8.6707467, 8.67259556, 8.81700098, 8.91865949],
[8.81223015, 8.67672238, 8.57444747, 8.49014736, 8.5183922,
8.49014736, 8.57444747, 8.67672238, 8.81223015],
[8.68698864, 8.56159881, 8.51774523, 8.44708824, 8.44391005,
8.44708824, 8.51774523, 8.56159881, 8.68698864],
[8.67600489, 8.57788943, 8.46484058, 8.41567031, 0.,
8.41567031, 8.46484058, 8.57788943, 8.67600489],
[8.68712127, 8.56155103, 8.51775668, 8.44706792, 8.44391005,
8.44706792, 8.51775668, 8.56155103, 8.68712127],
[8.81233708, 8.67673955, 8.5744255, 8.49017946, 8.5183922,
8.49017946, 8.5744255, 8.67673955, 8.81233708],
[8.91894005, 8.81708413, 8.67270074, 8.67075389, 8.65288076,
8.67075389, 8.67270074, 8.81708413, 8.91894005]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
# Souce instance
# - Tectonic region: active
# - Mech is NM
source.setMechanism('NM')
source._tectonic_region = 'Active Shallow Crust'
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[1.37668217, 1.14221705, 0.96668127, 0.86561026, 0.8316382,
0.86561026, 0.96668127, 1.14221705, 1.37668217],
[1.03033842, 0.7899535, 0.61128249, 0.50843465, 0.47310349,
0.50843465, 0.61128249, 0.7899535, 1.03033842],
[0.77980196, 0.53301769, 0.35495992, 0.24706995, 0.21231668,
0.24706995, 0.35495992, 0.53301769, 0.77980196],
[0.62353293, 0.37628889, 0.19897762, 0.09028387, 0.05409271,
0.09028387, 0.19897762, 0.37628889, 0.62353293],
[0.57300663, 0.32596438, 0.14586493, 0.03728933, 0.,
0.03728933, 0.14586493, 0.32596438, 0.57300663],
[0.62364878, 0.37634892, 0.19900584, 0.09029076, 0.05409271,
0.09029076, 0.19900584, 0.37634892, 0.62364878],
[0.78002107, 0.53314026, 0.35501415, 0.24708481, 0.21231668,
0.24708481, 0.35501415, 0.53314026, 0.78002107],
[1.03067234, 0.79013775, 0.61136979, 0.50845534, 0.47310349,
0.50845534, 0.61136979, 0.79013775, 1.03067234]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[8.24181179, 8.13246903, 7.99744833, 7.97427973, 7.96488994,
7.97427973, 7.99744833, 8.13246903, 8.24181179],
[8.0523808, 7.94857792, 7.8097532, 7.80275055, 7.78489184,
7.80275055, 7.8097532, 7.94857792, 8.0523808],
[7.94376991, 7.80938707, 7.70839546, 7.62713392, 7.65061937,
7.62713392, 7.70839546, 7.80938707, 7.94376991],
[7.82330022, 7.69799123, 7.64933048, 7.58013644, 7.57530397,
7.58013644, 7.64933048, 7.69799123, 7.82330022],
[7.81082127, 7.7099614, 7.59905656, 7.54904995, 0.,
7.54904995, 7.59905656, 7.7099614, 7.81082127],
[7.8234252, 7.69795153, 7.64934225, 7.58011859, 7.57530397,
7.58011859, 7.64934225, 7.69795153, 7.8234252],
[7.94387723, 7.80940883, 7.70837861, 7.62716348, 7.65061937,
7.62716348, 7.70837861, 7.80940883, 7.94387723],
[8.05265024, 7.94866209, 7.80985208, 7.80275807, 7.78489184,
7.80275807, 7.80985208, 7.94866209, 8.05265024]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
# Souce instance
# - Tectonic region: active
# - Mech is SS
source.setMechanism('SS')
source._tectonic_region = 'Active Shallow Crust'
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[2.87579088, 2.5456044, 2.29185071, 2.12884849, 2.07268393,
2.12884849, 2.29185071, 2.5456044, 2.87579088],
[2.38460904, 2.00333625, 1.69987873, 1.49778447, 1.42682201,
1.49778447, 1.69987873, 2.00333625, 2.38460904],
[1.98637252, 1.54679373, 1.17582687, 0.914992, 0.81812345,
0.914992, 1.17582687, 1.54679373, 1.98637252],
[1.72104847, 1.22626178, 0.78021345, 0.43426687, 0.29180829,
0.43426687, 0.78021345, 1.22626178, 1.72104847],
[1.62592524, 1.10672645, 0.62338097, 0.21787328, 0.,
0.21787328, 0.62338097, 1.10672645, 1.62592524],
[1.72124839, 1.22640328, 0.7802939, 0.43429295, 0.29180829,
0.43429295, 0.7802939, 1.22640328, 1.72124839],
[1.98673898, 1.54703738, 1.17595553, 0.9150287, 0.81812345,
0.9150287, 1.17595553, 1.54703738, 1.98673898],
[2.3850933, 2.00364385, 1.7000298, 1.49782584, 1.42682201,
1.49782584, 1.7000298, 2.00364385, 2.3850933]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[8.37718818, 8.22723595, 8.0625333, 8.01740393, 8.00044835,
8.01740393, 8.0625333, 8.22723595, 8.37718818],
[8.12814884, 7.97494557, 7.79806013, 7.76281765, 7.735536,
7.76281765, 7.79806013, 7.97494557, 8.12814884],
[7.96791548, 7.77614524, 7.62644695, 7.5120851, 7.52225031,
7.5120851, 7.62644695, 7.77614524, 7.96791548],
[7.81413881, 7.62326525, 7.51608628, 7.40624352, 7.38577101,
7.40624352, 7.51608628, 7.62326525, 7.81413881],
[7.78868149, 7.61851685, 7.44728324, 7.35263272, 0.,
7.35263272, 7.44728324, 7.61851685, 7.78868149],
[7.81428779, 7.62324614, 7.51610811, 7.40622914, 7.38577101,
7.40622914, 7.51610811, 7.62324614, 7.81428779],
[7.96807069, 7.77620063, 7.62644821, 7.51211876, 7.52225031,
7.51211876, 7.62644821, 7.77620063, 7.96807069],
[8.12847458, 7.97507013, 7.798177, 7.76283076, 7.735536,
7.76283076, 7.798177, 7.97507013, 8.12847458]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
# Souce instance
# - Tectonic region: stable
# - Mech is all
source.setMechanism('ALL')
source._tectonic_region = 'Stable Shallow Crust'
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[2.03925124, 1.73155737, 1.4995708, 1.36023359, 1.3129796,
1.36023359, 1.4995708, 1.73155737, 2.03925124],
[1.58388219, 1.25490086, 1.00456938, 0.85266727, 0.80013341,
0.85266727, 1.00456938, 1.25490086, 1.58388219],
[1.24073997, 0.8891456, 0.621322, 0.4507253, 0.39310934,
0.4507253, 0.621322, 0.8891456, 1.24073997],
[1.02181451, 0.65483395, 0.37085259, 0.18246645, 0.11472522,
0.18246645, 0.37085259, 0.65483395, 1.02181451],
[0.94836452, 0.57566287, 0.28111922, 0.08186392, 0.,
0.08186392, 0.28111922, 0.57566287, 0.94836452],
[1.02197754, 0.65492818, 0.37089972, 0.18247916, 0.11472522,
0.18247916, 0.37089972, 0.65492818, 1.02197754],
[1.24104568, 0.88932733, 0.62140729, 0.45074894, 0.39310934,
0.45074894, 0.62140729, 0.88932733, 1.24104568],
[1.58432388, 1.2551578, 1.00469232, 0.85269799, 0.80013341,
0.85269799, 1.00469232, 1.2551578, 1.58432388]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[6.77027261, 6.6175234, 6.4620818, 6.41487409, 6.39777405,
6.41487409, 6.4620818, 6.6175234, 6.77027261],
[6.52306411, 6.37308879, 6.21401632, 6.17868645, 6.15460391,
6.17868645, 6.21401632, 6.37308879, 6.52306411],
[6.36643129, 6.19096821, 6.06108712, 5.96682577, 5.97487576,
5.96682577, 6.06108712, 6.19096821, 6.36643129],
[6.22803711, 6.05926472, 5.97000316, 5.88510619, 5.87109182,
5.88510619, 5.97000316, 6.05926472, 6.22803711],
[6.2037114, 6.05384337, 5.91573235, 5.84608701, 0.,
5.84608701, 5.91573235, 6.05384337, 6.2037114],
[6.22816728, 6.05925153, 5.97002045, 5.88509436, 5.87109182,
5.88509436, 5.97002045, 6.05925153, 6.22816728],
[6.36657773, 6.1910207, 6.0610899, 5.96685295, 5.97487576,
5.96685295, 6.0610899, 6.1910207, 6.36657773],
[6.52336953, 6.37320723, 6.21411804, 6.17869829, 6.15460391,
6.17869829, 6.21411804, 6.37320723, 6.52336953]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
# Souce instance
# - Tectonic region: stable
# - Mech is RS
source.setMechanism('RS')
source._tectonic_region = 'Stable Shallow Crust'
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[1.82771374, 1.52643611, 1.30046931, 1.16760601, 1.12277703,
1.16760601, 1.30046931, 1.52643611, 1.82771374],
[1.38246113, 1.06775489, 0.83169032, 0.69344486, 0.645892,
0.69344486, 0.83169032, 1.06775489, 1.38246113],
[1.05435251, 0.72652464, 0.48639585, 0.33959401, 0.2920419,
0.33959401, 0.48639585, 0.72652464, 1.05435251],
[0.84788911, 0.51540522, 0.27378007, 0.12450057, 0.07451822,
0.12450057, 0.27378007, 0.51540522, 0.84788911],
[0.78032378, 0.44695345, 0.20099496, 0.05123817, 0.,
0.05123817, 0.20099496, 0.44695345, 0.78032378],
[0.84804229, 0.51548687, 0.2738187, 0.12451008, 0.07451822,
0.12451008, 0.2738187, 0.51548687, 0.84804229],
[1.0546418, 0.72668956, 0.48646962, 0.33961424, 0.2920419,
0.33961424, 0.48646962, 0.72668956, 1.0546418],
[1.38289108, 1.06799812, 0.83180576, 0.6934727, 0.645892,
0.6934727, 0.83180576, 1.06799812, 1.38289108]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[6.73175745, 6.58096329, 6.42717448, 6.38198779, 6.36563579,
6.38198779, 6.42717448, 6.58096329, 6.73175745],
[6.48753618, 6.34187696, 6.18689857, 6.15537676, 6.13263668,
6.15537676, 6.18689857, 6.34187696, 6.48753618],
[6.33544657, 6.1667275, 6.04409157, 5.9555343, 5.96586226,
5.9555343, 6.04409157, 6.1667275, 6.33544657],
[6.20063145, 6.04113522, 5.96186983, 5.88482917, 5.87387645,
5.88482917, 5.96186983, 6.04113522, 6.20063145],
[6.17796118, 6.03838637, 5.91117189, 5.85034216, 0.,
5.85034216, 5.91117189, 6.03838637, 6.17796118],
[6.20075891, 6.04111884, 5.96188527, 5.88481675, 5.87387645,
5.88481675, 5.96188527, 6.04111884, 6.20075891],
[6.3355881, 6.16677535, 6.04409147, 5.9555607, 5.96586226,
5.9555607, 6.04409147, 6.16677535, 6.3355881],
[6.48783842, 6.34199129, 6.18699823, 6.15538781, 6.13263668,
6.15538781, 6.18699823, 6.34199129, 6.48783842]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
# Souce instance
# - Tectonic region: stable
# - Mech is NM
source.setMechanism('NM')
source._tectonic_region = 'Stable Shallow Crust'
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[1.90163976, 1.59090426, 1.35772802, 1.21975997, 1.17315714,
1.21975997, 1.35772802, 1.59090426, 1.90163976],
[1.44234875, 1.11595412, 0.87047978, 0.72608143, 0.6763976,
0.72608143, 0.87047978, 1.11595412, 1.44234875],
[1.10201987, 0.7606424, 0.50965507, 0.35594865, 0.30612448,
0.35594865, 0.50965507, 0.7606424, 1.10201987],
[0.88732736, 0.5400275, 0.28698884, 0.13052973, 0.0781228,
0.13052973, 0.28698884, 0.5400275, 0.88732736],
[0.81684854, 0.46835876, 0.21071547, 0.05370847, 0.,
0.05370847, 0.21071547, 0.46835876, 0.81684854],
[0.88748668, 0.54011298, 0.28702933, 0.1305397, 0.0781228,
0.1305397, 0.28702933, 0.54011298, 0.88748668],
[1.10232064, 0.76081471, 0.50973231, 0.35596982, 0.30612448,
0.35596982, 0.50973231, 0.76081471, 1.10232064],
[1.44279245, 1.116207, 0.87059985, 0.72611052, 0.6763976,
0.72611052, 0.87059985, 1.116207, 1.44279245]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[6.51552789, 6.35105562, 6.18910077, 6.13678714, 6.11808742,
6.13678714, 6.18910077, 6.35105562, 6.51552789],
[6.25219098, 6.09163676, 5.92783781, 5.88815939, 5.8632258,
5.88815939, 5.92783781, 6.09163676, 6.25219098],
[6.08458377, 5.9012656, 5.76798442, 5.67370059, 5.68021406,
5.67370059, 5.76798442, 5.9012656, 6.08458377],
[5.9420166, 5.76720934, 5.67516593, 5.59187321, 5.57813972,
5.59187321, 5.67516593, 5.76720934, 5.9420166],
[5.91572554, 5.75981247, 5.62205686, 5.55427369, 0.,
5.55427369, 5.62205686, 5.75981247, 5.91572554],
[5.9421484, 5.76719951, 5.67518325, 5.59186206, 5.57813972,
5.59186206, 5.67518325, 5.76719951, 5.9421484],
[6.0847386, 5.90132265, 5.76798949, 5.67372705, 5.68021406,
5.67372705, 5.76798949, 5.90132265, 6.0847386],
[6.25250826, 6.09176251, 5.92794056, 5.88817184, 5.8632258,
5.88817184, 5.92794056, 6.09176251, 6.25250826]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
# Souce instance
# - Tectonic region: stable
# - Mech is SS
source.setMechanism('SS')
source._tectonic_region = 'Stable Shallow Crust'
dists = Distance.fromSites(gmpe, source, site)
dctx = dists.getDistanceContext()
rjb = np.array(
[[2.72873417, 2.37144886, 2.09954885, 1.92851977, 1.86995227,
1.92851977, 2.09954885, 2.37144886, 2.72873417],
[2.19864742, 1.7978181, 1.48474703, 1.28392745, 1.21396452,
1.28392745, 1.48474703, 1.7978181, 2.19864742],
[1.78020402, 1.3323943, 0.97123526, 0.72919945, 0.64364445,
0.72919945, 0.97123526, 1.3323943, 1.78020402],
[1.5064397, 1.01846151, 0.61039475, 0.3191157, 0.20751973,
0.3191157, 0.61039475, 1.01846151, 1.5064397],
[1.41089093, 0.90673813, 0.47477623, 0.15160737, 0.,
0.15160737, 0.47477623, 0.90673813, 1.41089093],
[1.50664467, 1.01859418, 0.61046522, 0.3191364, 0.20751973,
0.3191364, 0.61046522, 1.01859418, 1.50664467],
[1.7805844, 1.33263554, 0.97135557, 0.72923321, 0.64364445,
0.72923321, 0.97135557, 1.33263554, 1.7805844],
[2.1991657, 1.79813761, 1.48490175, 1.28396831, 1.21396452,
1.28396831, 1.48490175, 1.79813761, 2.1991657]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
rrup = np.array(
[[5.84874652, 5.62706324, 5.42929028, 5.34392626, 5.31406828,
5.34392626, 5.42929028, 5.62706324, 5.84874652],
[5.50458234, 5.27459324, 5.06456452, 4.9821571, 4.94467172,
4.9821571, 5.06456452, 5.27459324, 5.50458234],
[5.26448475, 5.00483041, 4.80820132, 4.67381918, 4.65867853,
4.67381918, 4.80820132, 5.00483041, 5.26448475],
[5.08135199, 4.81923672, 4.64680257, 4.514164, 4.47986499,
4.514164, 4.64680257, 4.81923672, 5.08135199],
[5.03620112, 4.78581905, 4.57286829, 4.44979468, 0.,
4.44979468, 4.57286829, 4.78581905, 5.03620112],
[5.0815089, 4.81926166, 4.64683299, 4.51415954, 4.47986499,
4.51415954, 4.64683299, 4.81926166, 5.0815089],
[5.26470503, 5.00493679, 4.80823501, 4.67384847, 4.65867853,
4.67384847, 4.80823501, 5.00493679, 5.26470503],
[5.5049661, 5.27477487, 5.06468553, 4.98217732, 4.94467172,
4.98217732, 5.06468553, 5.27477487, 5.5049661]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
def test_ss3_m6():
magnitude = 6.0
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 = rupture.QuadRupture.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(0, 20, 6)
y = np.linspace(0, 90, 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=1.0)
# Test fd
fd = test1.getFd()
fd_test = np.array(
[[ 0.05853668, 0.05032323, 0.0306438 , 0.00839635, -0.01102162,
-0.02621319],
[ 0.01720501, -0.00687296, -0.03804823, -0.05547473, -0.0644932 ,
-0.06947135],
[-0.03000065, -0.07006634, -0.07708165, -0.07865941, -0.0792369 ,
-0.07950887],
[ 0.0398062 , 0.02571145, -0.0018651 , -0.0255418 , -0.04176278,
-0.05235095],
[ 0.0696989 , 0.06389524, 0.04890304, 0.02983134, 0.01098535,
-0.00545921],
[ 0.088278 , 0.08511069, 0.07628596, 0.06350294, 0.04875897,
0.03373495],
[ 0.10179334, 0.09978475, 0.09401676, 0.0851842 , 0.07422509,
0.06210369],
[ 0.11242209, 0.11102701, 0.10696056, 0.10055471, 0.09229027,
0.08271454],
[ 0.12118279, 0.12015315, 0.11712653, 0.11228058, 0.10588323,
0.09825795],
[ 0.12785957, 0.12706892, 0.12473264, 0.12095384, 0.11589197,
0.10974684],
[ 0.12785908, 0.12724852, 0.12543819, 0.12249026, 0.11850249,
0.11360047]])
np.testing.assert_allclose(
fd, fd_test, rtol=1e-4)
def test_san_fernando():
# This is a challenging fault due to overlapping and discordant
# segments, as brought up by Graeme Weatherill. Our initial
# implementation put the origin on the wrong side of the fault.
x0 = np.array([7.1845, 7.8693])
y0 = np.array([-10.3793, -16.2096])
z0 = np.array([3.0000, 0.0000])
x1 = np.array([-7.8506, -7.5856])
y1 = np.array([-4.9073, -12.0682])
z1 = np.array([3.0000, 0.0000])
x2 = np.array([-4.6129, -5.5149])
y2 = np.array([3.9887, -4.3408])
z2 = np.array([16.0300, 8.0000])
x3 = np.array([10.4222, 9.9400])
y3 = np.array([-1.4833, -8.4823])
z3 = np.array([16.0300, 8.0000])
epilat = 34.44000
epilon = -118.41000
proj = geo.utils.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)
flt._segment_index = [0, 1]
# Make a source object; most of the 'event' values don't matter
event = {'lat': 0, 'lon': 0, 'depth':0, 'mag': 6.61,
'id':'', 'locstring':'', 'type':'U',
'time':ShakeDateTime.utcfromtimestamp(int(time.time())),
'timezone':'UTC'}
source = Source(event, flt)
# Grid of sites
buf = 0.25
lat = np.linspace(np.nanmin(flt._lat)-buf, np.nanmax(flt._lat)+buf, 10)
lon = np.linspace(np.nanmin(flt._lon)-buf, np.nanmax(flt._lon)+buf, 10)
lons, lats = np.meshgrid(lon, lat)
dep = np.zeros_like(lons)
x,y = proj(lon, lat)
fltx,flty = proj(flt._lon, flt._lat)
# Calculate U and T
dtypes = ['U', 'T']
dists = get_distance(dtypes, lats, lons, dep, source)
targetU = np.array(
[[ 29.37395812, 22.56039569, 15.74545461, 8.92543078,
2.09723735, -4.73938823, -11.58093887, -18.42177264,
-25.25743913, -32.08635501],
[ 31.84149137, 25.03129417, 18.22007124, 11.40292429,
4.57583886, -2.26009972, -9.09790123, -15.92911065,
-22.75071243, -29.56450963],
[ 34.30623138, 27.49382948, 20.67774678, 13.85111535,
7.0115472 , 0.16942111, -6.65327488, -13.45181115,
-20.24352643, -27.03530618],
[ 36.78170249, 29.96380633, 23.1270492 , 16.23906653,
9.32934682, 2.41729624, -4.2732657 , -10.94940844,
-17.703852 , -24.4792072 ],
[ 39.29233805, 32.49155866, 25.68380903, 18.73823089,
12.08780156, 5.99219619, -1.38387344, -8.28331275,
-15.08759643, -21.87909368],
[ 41.84662959, 35.09745097, 28.42432401, 21.98993679,
15.2994003 , 8.38037254, 1.3900846 , -5.5601922 ,
-12.4250749 , -19.24690137],
[ 44.41552101, 37.69652131, 31.0257236 , 24.38573309,
17.67059825, 10.84688716, 3.96604399, -2.920931 ,
-9.78152208, -16.6132751 ],
[ 46.97201328, 40.2558351 , 33.55821495, 26.85923974,
20.12416451, 13.33640001, 6.50905851, -0.33349597,
-7.17138975, -13.99568321],
[ 49.51154107, 42.79053584, 36.07536907, 29.35382731,
22.61099757, 15.83894006, 9.04135415, 2.22928601,
-4.58574545, -11.3959888 ],
[ 52.03832734, 45.31289877, 38.58842009, 31.85764151,
25.11309728, 18.35066231, 11.57145669, 4.78070229,
-2.01505508, -8.81029694]])
np.testing.assert_allclose(dists['U'], targetU)
targetT = np.array(
[[-40.32654805, -38.14066537, -35.95781299, -33.79265063,
-31.65892948, -29.56075203, -27.48748112, -25.41823592,
-23.33452174, -21.22822801],
[-32.28894353, -30.06603457, -27.83163648, -25.61482279,
-23.45367121, -21.36959238, -19.34738882, -17.33510593,
-15.28949735, -13.20224592],
[-24.30254163, -22.03532096, -19.70590091, -17.35907062,
-15.10840929, -13.02682541, -11.13554925, -9.25705749,
-7.26675455, -5.19396824],
[-16.41306482, -14.1418547 , -11.68888578, -8.9318195 ,
-6.39939727, -4.10984325, -2.85061088, -1.29211846,
0.68929792, 2.78115216],
[ -8.63784529, -6.5089946 , -4.32108309, -1.44275161,
-0.05102145, -0.20890633, 3.92700516, 6.36977183,
8.55572399, 10.72128633],
[ -0.88135778, 1.06766314, 2.77955566, 3.8241835 ,
5.99212478, 8.76823285, 11.54715599, 14.0961506 ,
16.4200502 , 18.65346494],
[ 6.98140207, 8.91888936, 10.77724993, 12.6499521 ,
14.79454638, 17.18482779, 19.63520498, 22.03525644,
24.35152986, 26.60592498],
[ 14.95635952, 16.95134069, 18.94768299, 20.99811237,
23.15975573, 25.42700742, 27.74302905, 30.0547134 ,
32.33583361, 34.58421221],
[ 22.9921068 , 25.0353212 , 27.09829391, 29.20364631,
31.3678744 , 33.58684524, 35.8383652 , 38.09736043,
40.34713771, 42.58152772],
[ 31.05186177, 33.1252095 , 35.21960344, 37.34488267,
39.50633206, 41.70076344, 43.91762786, 46.14415669,
48.37021739, 50.59029205]])
np.testing.assert_allclose(dists['T'], targetT)
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_timeutils():
time = 1470152355.244582
sdt = ShakeDateTime.utcfromtimestamp(time)
assert sdt == ShakeDateTime(2016, 8, 2, 15, 39, 15, 244582)
def test_distance_from_sites_source():
# Make sites instance
vs30file = os.path.join(shakedir, 'tests/data/Vs30_test.grd')
cx = -118.2
cy = 34.1
dx = 0.0083
dy = 0.0083
xspan = 0.0083 * 5
yspan = 0.0083 * 5
site = Sites.createFromCenter(cx, cy, xspan, yspan, dx, dy,
vs30File=vs30file,
padding=True, resample=False)
# Make souce instance
lat0 = np.array([34.1])
lon0 = np.array([-118.2])
lat1 = np.array([34.2])
lon1 = np.array([-118.15])
z = np.array([1.0])
W = np.array([3.0])
dip = np.array([30.])
flt = Fault.fromTrace(lon0, lat0, lon1, lat1, z, W, dip)
event = {'lat': 34.1, 'lon': -118.2, 'depth': 1, 'mag': 6,
'id': '', 'locstring': '', 'type': 'U',
'time': ShakeDateTime.utcfromtimestamp(int(time.time())),
'timezone': 'UTC'}
source = Source(event, flt)
gmpelist = [AbrahamsonEtAl2014(), BergeThierryEtAl2003SIGMA()]
dists = Distance.fromSites(gmpelist, source, site)
dctx = dists.getDistanceContext()
rhypo = np.array(
[[4.91471497, 4.47602418, 4.13427127, 3.91492565, 3.83902646,
3.91492565, 4.13427127, 4.47602418, 4.91471497],
[4.25968568, 3.74498133, 3.32896405, 3.05225679, 2.95426722,
3.05225679, 3.32896405, 3.74498133, 4.25968568],
[3.7219197, 3.11965436, 2.60558436, 2.24124201, 2.10583262,
2.24124201, 2.60558436, 3.11965436, 3.7219197],
[3.35823105, 2.67523213, 2.05265767, 1.564393, 1.36331682,
1.564393, 2.05265767, 2.67523213, 3.35823105],
[3.22800413, 2.50973226, 1.83166664, 1.26045653, 1.,
1.26045653, 1.83166664, 2.50973226, 3.22800413],
[3.35850726, 2.67542717, 2.05277065, 1.56443006, 1.36331682,
1.56443006, 2.05277065, 2.67542717, 3.35850726],
[3.72241812, 3.11998886, 2.60576236, 2.24129374, 2.10583262,
2.24129374, 2.60576236, 3.11998886, 3.72241812],
[4.26033893, 3.74539929, 3.32917303, 3.05231378, 2.95426722,
3.05231378, 3.32917303, 3.74539929, 4.26033893]]
)
np.testing.assert_allclose(
rhypo, dctx.rhypo, rtol=0, atol=0.01)
rx = np.array(
[[-4.25168879e+00, -3.54281195e+00, -2.83395897e+00,
-2.12512986e+00, -1.41632463e+00, -7.07543303e-01,
1.21411306e-03, 7.09947601e-01, 1.41865714e+00],
[-3.89788718e+00, -3.18894046e+00, -2.48001760e+00,
-1.77111861e+00, -1.06224350e+00, -3.53392295e-01,
3.55434994e-01, 1.06423835e+00, 1.77301777e+00],
[-3.54408550e+00, -2.83506891e+00, -2.12607617e+00,
-1.41710732e+00, -7.08162347e-01, 7.58720005e-04,
7.09655868e-01, 1.41852908e+00, 2.12737835e+00],
[-3.19028373e+00, -2.48119729e+00, -1.77213470e+00,
-1.06309600e+00, -3.54081177e-01, 3.54909735e-01,
1.06387673e+00, 1.77281978e+00, 2.48173889e+00],
[-2.83648190e+00, -2.12732562e+00, -1.41819320e+00,
-7.09084651e-01, 2.56777675e-12, 7.09060743e-01,
1.41809756e+00, 2.12711045e+00, 2.83609938e+00],
[-2.48268001e+00, -1.77345390e+00, -1.06425166e+00,
-3.55073292e-01, 3.54081177e-01, 1.06321174e+00,
1.77231837e+00, 2.48140106e+00, 3.19045980e+00],
[-2.12887807e+00, -1.41958215e+00, -7.10310097e-01,
-1.06192604e-03, 7.08162347e-01, 1.41736271e+00,
2.12653914e+00, 2.83569163e+00, 3.54482016e+00],
[-1.77507608e+00, -1.06571037e+00, -3.56368521e-01,
3.52949440e-01, 1.06224350e+00, 1.77151365e+00,
2.48075986e+00, 3.18998213e+00, 3.89918045e+00]]
)
np.testing.assert_allclose(
rx, dctx.rx, rtol=0, atol=0.01)
rjb = np.array(
[[4.25806540e+00, 3.54812473e+00, 2.83820819e+00,
2.12831587e+00, 1.41844799e+00, 7.08605677e-01,
2.52145001e-03, 2.71336156e-03, 2.81274718e-03],
[3.90373176e+00, 3.19372137e+00, 2.48373511e+00,
1.77377308e+00, 1.06383562e+00, 3.53925643e-01,
2.25816823e-03, 2.45009861e-03, 2.54949398e-03],
[3.54939857e+00, 2.83931844e+00, 2.12926243e+00,
1.41923064e+00, 7.09223517e-01, 1.57594916e-03,
1.86044244e-03, 2.05239165e-03, 2.15179678e-03],
[3.21162971e+00, 2.48510934e+00, 1.77479025e+00,
1.06468863e+00, 3.54611655e-01, 1.04375185e-03,
1.32827303e-03, 1.52024106e-03, 1.61965594e-03],
[3.07587955e+00, 2.30690967e+00, 1.53793979e+00,
7.68969896e-01, 5.88918451e-12, 3.77111295e-04,
6.61660373e-04, 8.53647223e-04, 2.38384209e-01],
[3.21191809e+00, 2.48531877e+00, 1.79442084e+00,
1.20242597e+00, 8.54793253e-01, 5.62052963e-01,
2.69254693e-01, 5.26105100e-05, 5.93270976e-01],
[3.58897017e+00, 2.95646628e+00, 2.40489915e+00,
2.00231070e+00, 1.70958533e+00, 1.41681634e+00,
1.12398937e+00, 8.63761551e-01, 1.08872127e+00],
[4.14172611e+00, 3.60741953e+00, 3.17112489e+00,
2.85711592e+00, 2.56437623e+00, 2.27157856e+00,
1.97872291e+00, 1.78518260e+00, 1.90424524e+00]]
)
np.testing.assert_allclose(
rjb, dctx.rjb, rtol=0, atol=0.01)
ry0 = np.array(
[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[3.15917743e-01, 2.29488608e-02, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[1.17233848e+00, 8.79340738e-01, 5.86285075e-01,
2.93171494e-01, 6.20905972e-12, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[2.02875906e+00, 1.73573247e+00, 1.44264795e+00,
1.14950551e+00, 8.56305149e-01, 5.63046877e-01,
2.69730699e-01, 0.00000000e+00, 0.00000000e+00],
[2.88517947e+00, 2.59212404e+00, 2.29901067e+00,
2.00583937e+00, 1.71261015e+00, 1.41932302e+00,
1.12597797e+00, 8.32575011e-01, 5.39114157e-01],
[3.74159969e+00, 3.44851542e+00, 3.15537321e+00,
2.86217307e+00, 2.56891499e+00, 2.27559900e+00,
1.98222508e+00, 1.68879326e+00, 1.39530352e+00]]
)
np.testing.assert_allclose(
ry0, dctx.ry0, rtol=0, atol=0.01)
rrup = np.array(
[[4.37398161, 3.68659567, 3.00969158, 2.35231374, 1.73674834,
1.22755262, 1.0025215, 1.22371894, 1.57836451],
[4.02976949, 3.34678672, 2.67788811, 2.03697073, 1.46129187,
1.06271102, 1.06352692, 1.40073832, 1.75541869],
[3.68743926, 3.01030105, 2.3526499, 1.73673635, 1.22706347,
1.00157564, 1.22283363, 1.57764099, 1.93235615],
[3.3634728, 2.67858182, 2.03712377, 1.46095502, 1.06170931,
1.06220616, 1.39958479, 1.75442695, 2.1091769],
[3.23412325, 2.51415965, 1.8343632, 1.26143652, 1.,
1.2212501, 1.57621925, 1.9310962, 2.28588093],
[3.36374812, 2.67877609, 2.05412785, 1.56384179, 1.3617346,
1.50608502, 1.77308319, 2.10764873, 2.46246823],
[3.72541099, 3.12078859, 2.6043486, 2.23799413, 2.09885629,
2.11696797, 2.23191013, 2.4299612, 2.69282837],
[4.26042286, 3.74318473, 3.32482368, 3.04635272, 2.9183523,
2.86659485, 2.88815116, 2.98141559, 3.13998993]]
)
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
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_chichi_with_get_distance():
# read in fault file
f = os.path.join(shakedir, 'tests/data/0137A.POL')
i0 = np.arange(0, 9 * 11 * 3, 11)
i1 = i0 + 10
cs = list(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)
# event information doesn't matter except hypocenter
event = {'lat': 23.85, 'lon': 120.82, 'depth': 8, 'mag': 7.62,
'id': '', 'locstring': '', 'type': 'U',
'time': ShakeDateTime.utcfromtimestamp(int(time.time())),
'timezone': 'UTC'}
source = Source(event, flt)
# Get NGA distances
distfile = os.path.join(shakedir, 'tests/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()
nga_T = df2['T'].as_matrix()
nga_U = df2['U'].as_matrix()
test_ry = np.array([
-49.25445446, -76.26871272, -37.1288192, -53.47792996,
-50.30711637, -63.96322125, -61.01988704, -81.2001781,
-76.00646939, -74.39038054, -92.23617124, -90.66976945,
-89.68551411, -102.98798328, -114.70036085, -29.83636082,
-28.50133134, -27.86922916, -36.00619214, -44.68826209,
-47.64580208, -53.92619079, -59.11962858, -55.90584822,
-55.00772025, -48.81756715, -59.27542007, -62.13633659,
-70.0673351, -75.96977638, -61.6959293, -60.34564074,
-81.49792285, -78.75933138, -80.80533738, -85.24473008,
-94.07519297, -93.75010471, -96.87089883, -100.06112271,
-98.86980873, -95.92330113, -107.44086722, -119.1065369,
-120.60405905, -113.42995442, -115.94930662, -115.2398216,
-107.37840927, -49.25445446, -48.78386688, -108.49133002,
-88.03303353, -44.66653428, -81.04476548, -38.26801619,
-70.51178983, -69.15679931, -74.74562139, -86.51133446,
-27.62153029, -48.33279375, -30.0808298, -113.98345018,
-97.96609537, -87.9863122, -39.45970018, -80.1387617,
-42.27121388, -82.05027834, -81.55987067, -81.55987067,
-107.25255717, 67.62695516, -3.27797047, -197.98554369,
82.30996151, 18.42180605, -22.88851072, -35.75245916,
-19.54788146, -18.19780517, 19.85077702, 20.33310282,
19.95448398, 20.55508903, 18.17428572, 17.87997374,
16.97323804, 16.0025885, 13.88001846, 18.42180605,
-3.27797047, 51.43098894, 28.97695533, -53.20579538,
38.7537468, 33.48878882, 26.25189111, 22.54251612,
13.37141837, -5.80928302, -6.68056794, -14.50860117,
-15.23992093, -27.63281952, -11.66075049, -36.94595337,
-40.97168031, -41.2814342, -48.64456898, -61.55777751,
-11.15038984, -17.16482959, 55.84202839, 36.78540588,
21.18550074, 19.14658833, 19.22680282, 5.76327358,
-47.45309937, -44.33194991, -55.15852372, 37.33066096,
37.64135657, 14.31598698, 4.60495737, 6.87107021,
18.42180605, 113.59285783, 109.06420877, 104.23416509,
99.21599973, 95.25204545, 90.29487934, 86.26977557,
95.28705209, 87.12907925, 101.40561896, 96.68858152,
92.90287952, 100.36659012, 97.19448577, 92.8627461,
85.01448355, 93.36767736, 96.90824009, 86.48002825,
88.71037964, 106.17282325, 102.56142319, 97.60004093,
99.61798574, 97.36337239, 94.22000798, 86.99488734,
90.05981676, 90.51189502, 100.7166391, 100.31931988,
67.62695516, 94.15062409, 87.77053675, 124.21806013,
99.23108884, 101.48199452, 92.63771423, 78.88723272,
72.7261356, 80.58682246, 73.30258213, 70.20783518,
60.57963211, -87.72265602, -148.10933308, -150.41334959,
-144.12558375, -145.5625388, -132.09479688, -135.12980144,
-121.10883695, -143.75755221, -117.73616176, -115.28563276,
-138.79652905, -143.10405603, -151.78419035, -159.75299736,
-149.69457229, -175.20332448, -181.00970647, -188.86536942,
-176.88178468, -194.20978527, -204.54944453, -161.04413103,
-197.98554369, -96.74089367, -133.49237232, -84.71198922,
-164.97719097, -202.48241157, -74.54550169, -147.37402934,
-144.64074441, -147.94282804, -122.80976842, -133.1671346,
-136.3051809, -113.93174768, -151.02125407, -146.5198829,
-156.19720713, -126.06138725, -131.44422964, -197.62591198,
-204.42320856, -149.84576063, -121.56474664, -130.99947339,
-148.41767074, -145.28448367, 104.58903799, 82.1649906,
67.69977397, 39.46989193, -69.00949731, -133.49237232,
-128.264754, -84.71198922, -108.49133002, 119.86128724,
122.73556155, 126.28254009, 125.12436373, 123.32498578,
123.8337768, 121.39931427, 121.48412837, 122.03669249,
122.59675818, 119.54338365, 120.33961222, 120.69581745,
116.96928355, 117.6687724, 116.62277942, 115.39650689,
112.60751523, 109.82643069, 108.2500678, 130.9143614,
126.50049543, 112.76229057, 132.76840098, 107.27099883,
128.16063464, 123.83157143, 120.46711628, 112.55756637,
135.59953867, 136.66138116, 136.98573162, 134.04528777,
116.27744752, 129.2794577, 119.13550981, 124.67196321,
130.9728774, 130.9039439, 128.70028371, 130.04592892,
140.21819548, 140.60370422, 113.37585901, 123.21523323,
123.88149248, 128.56894995, 128.45186255, 118.74080853,
126.71189149, 119.79833338, 130.00866791, -160.01242472,
13.55424709, 110.26938756, 97.71987778, 110.93671325,
108.71965725, 105.03432063, 106.36049687, 99.27569343,
115.06168146, 77.00378531, 81.50139192, 92.15605815,
79.94311644, 83.16892433, 52.23389149, 50.97110177,
67.95167063, 63.43930833, 40.20494692, 43.22936492,
47.21513635, 38.94380012, 53.85489136, 56.69935207,
48.07036522, 64.46887891, 14.98020647, 17.35046801,
16.15236633, 14.41062231, 19.99605739, 18.31076661,
15.07058247, 12.34339267, 13.57621451, 14.72685201,
22.04539325, 20.47982142, 9.66768974, 8.05139052,
29.22924869, 3.75876894, 7.8610467, 29.20272495,
15.19325822, -2.38981899, 5.58349359, -0.62239018,
-4.38178769, -11.43651893, -20.07048519, -16.0588668,
82.30996151, 13.55424709, 104.49355303, -11.29628168,
82.1649906, 34.22207039, 38.08490923, -10.15855131,
111.0308369, 81.78397481, 73.56334665, 81.27164139,
74.55979012, 16.08437955, 23.8203941, 24.68836209,
28.73767914, 21.06714416, 19.44159522, 4.62135887,
3.41771413, 5.051121, -6.81834189, 6.40341853,
-0.35693923, -17.74409367, -8.91759817, -18.05278804,
7.70695248, -5.52733835, -16.02924961, -4.54310111,
-22.84234773, -1.71908199, 39.46989193, -14.74007542,
23.59992543, -10.49966883, -11.47733869, -22.8200901,
-9.72486483, 95.96997763, -115.36487081, -52.88924268,
-90.2275069, -132.22657274, -100.52455976, -115.24052939,
-113.84482359, -114.41088165, -114.63386688, -115.92829006,
-117.52597227, -114.49770514, -114.46881502, -76.26871272,
-115.36487081, -160.01242472, -110.6429636, -77.47722955,
-80.24672646, -85.90422427, -94.92075147, -102.44309541,
-106.23741455, -111.56110193, -115.13402727, -48.64043046,
-60.86151946, -66.52137871, -110.04628212, -75.27694696,
-78.87041369, -88.08700161, -90.18844188, -93.65776393,
-92.58976279, -107.31364843, -115.04064471, -125.98500718,
-75.9341032, -39.45970018, -14.74007542, -23.16835763])
test_ry0 = np.array([
5.38783354, 32.4020918, 0., 9.61130904,
6.44049545, 20.09660033, 17.15326613, 37.33355718,
32.13984847, 30.52375962, 48.36955032, 46.80314854,
45.81889319, 59.12136236, 70.83373993, 0.,
0., 0., 0., 0.82164117,
3.77918116, 10.05956987, 15.25300766, 12.0392273,
11.14109933, 4.95094623, 15.40879915, 18.26971567,
26.20071419, 32.10315546, 17.82930838, 16.47901983,
37.63130193, 34.89271046, 36.93871646, 41.37810916,
50.20857205, 49.88348379, 53.00427791, 56.19450179,
55.00318781, 52.05668021, 63.5742463, 75.23991598,
76.73743813, 69.5633335, 72.0826857, 71.37320068,
63.51178836, 5.38783354, 4.91724596, 64.6247091,
44.16641261, 0.79991336, 37.17814456, 0.,
26.64516892, 25.2901784, 30.87900047, 42.64471355,
0., 4.46617283, 0., 70.11682926,
54.09947445, 44.11969128, 0., 36.27214079,
0., 38.18365743, 37.69324975, 37.69324975,
63.38593626, 31.95985109, 0., 154.11892278,
46.64285745, 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 15.76388487, 0., 9.33917446,
3.08664273, 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 4.77794806, 17.69115659,
0., 0., 20.17492433, 1.11830182,
0., 0., 0., 0.,
3.58647845, 0.46532899, 11.2919028, 1.6635569,
1.97425251, 0., 0., 0.,
0., 77.92575377, 73.39710471, 68.56706103,
63.54889567, 59.58494138, 54.62777528, 50.6026715,
59.61994802, 51.46197518, 65.7385149, 61.02147746,
57.23577546, 64.69948606, 61.52738171, 57.19564204,
49.34737949, 57.7005733, 61.24113602, 50.81292419,
53.04327558, 70.50571919, 66.89431913, 61.93293686,
63.95088168, 61.69626833, 58.55290391, 51.32778327,
54.3927127, 54.84479095, 65.04953504, 64.65221582,
31.95985109, 58.48352003, 52.10343269, 88.55095607,
63.56398477, 65.81489046, 56.97061016, 43.22012866,
37.05903154, 44.9197184, 37.63547806, 34.54073112,
24.91252804, 43.85603511, 104.24271216, 106.54672867,
100.25896283, 101.69591788, 88.22817597, 91.26318052,
77.24221603, 99.89093129, 73.86954084, 71.41901185,
94.92990813, 99.23743511, 107.91756944, 115.88637645,
105.82795138, 131.33670356, 137.14308555, 144.9987485,
133.01516376, 150.34316435, 160.68282361, 117.17751011,
154.11892278, 52.87427275, 89.6257514, 40.8453683,
121.11057005, 158.61579065, 30.67888078, 103.50740842,
100.77412349, 104.07620713, 78.9431475, 89.30051368,
92.43855998, 70.06512676, 107.15463315, 102.65326198,
112.33058622, 82.19476634, 87.57760872, 153.75929106,
160.55658764, 105.97913971, 77.69812572, 87.13285248,
104.55104982, 101.41786275, 68.92193392, 46.49788654,
32.0326699, 3.80278787, 25.14287639, 89.6257514,
84.39813309, 40.8453683, 64.6247091, 84.19418317,
87.06845748, 90.61543602, 89.45725966, 87.65788171,
88.16667274, 85.73221021, 85.81702431, 86.36958842,
86.92965411, 83.87627959, 84.67250815, 85.02871339,
81.30217949, 82.00166833, 80.95567535, 79.72940282,
76.94041117, 74.15932662, 72.58296373, 95.24725733,
90.83339137, 77.0951865, 97.10129692, 71.60389476,
92.49353057, 88.16446736, 84.80001222, 76.89046231,
99.93243461, 100.9942771, 101.31862755, 98.37818371,
80.61034346, 93.61235363, 83.46840575, 89.00485915,
95.30577334, 95.23683984, 93.03317965, 94.37882485,
104.55109142, 104.93660016, 77.70875494, 87.54812917,
88.21438842, 92.90184589, 92.78475848, 83.07370447,
91.04478743, 84.13122931, 94.34156384, 116.14580381,
0., 74.60228349, 62.05277372, 75.26960919,
73.05255319, 69.36721657, 70.69339281, 63.60858937,
79.3945774, 41.33668124, 45.83428785, 56.48895409,
44.27601238, 47.50182027, 16.56678743, 15.30399771,
32.28456656, 27.77220427, 4.53784286, 7.56226086,
11.54803229, 3.27669605, 18.1877873, 21.032248,
12.40326116, 28.80177485, 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
46.64285745, 0., 68.82644897, 0.,
46.49788654, 0., 2.41780516, 0.,
75.36373283, 46.11687074, 37.89624258, 45.60453732,
38.89268605, 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 0., 0.,
0., 0., 3.80278787, 0.,
0., 0., 0., 0.,
0., 60.30287357, 71.49824989, 9.02262176,
46.36088598, 88.35995182, 56.65793884, 71.37390848,
69.97820268, 70.54426073, 70.76724596, 72.06166914,
73.65935135, 70.63108422, 70.6021941, 32.4020918,
71.49824989, 116.14580381, 66.77634268, 33.61060864,
36.38010555, 42.03760335, 51.05413055, 58.57647449,
62.37079364, 67.69448101, 71.26740635, 4.77380954,
16.99489854, 22.65475779, 66.1796612, 31.41032604,
35.00379277, 44.22038069, 46.32182096, 49.79114301,
48.72314188, 63.44702751, 71.1740238, 82.11838626,
32.06748228, 0., 0., 0.])
dist_types = ['repi', 'rhypo', 'rjb', 'rrup', 'rx', 'ry', 'ry0', 'U', 'T']
dists = get_distance(dist_types, slat, slon, sdep, source)
np.testing.assert_allclose(
nga_repi, dists['repi'], rtol=0, atol=2)
np.testing.assert_allclose(
nga_rhypo, dists['rhypo'], rtol=0, atol=2)
np.testing.assert_allclose(
nga_rjb, dists['rjb'], rtol=0, atol=2)
np.testing.assert_allclose(
nga_rrup, dists['rrup'], rtol=0, atol=2)
np.testing.assert_allclose(
nga_rx, dists['rx'], rtol=0, atol=2)
np.testing.assert_allclose(
test_ry, dists['ry'], rtol=0, atol=2)
np.testing.assert_allclose(
test_ry0, dists['ry0'], rtol=0, atol=2)
np.testing.assert_allclose(
nga_U, dists['U'], rtol=0, atol=6)
np.testing.assert_allclose(
nga_T, dists['T'], rtol=0, atol=2)
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_timeutils():
time = 1470152355.244582
sdt = ShakeDateTime.utcfromtimestamp(time)
assert sdt == ShakeDateTime(2016, 8, 2, 15, 39, 15, 244582)
def test_ss3_move_hypo1():
magnitude = 7.2
dip = np.array([90])
rake = 180.0
width = np.array([15])
rupx = np.array([0, 0])
rupy = np.array([0, 80])
zp = np.array([0.0])
epix = np.array([1.0])
epiy = np.array([-1.0])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(rupx, rupy, reverse=True)
epilon, epilat = proj(epix, epiy, reverse=True)
# Origin
origin = Origin({'lat':epilat[0],
'lon':epilon[0],
'depth':-1,
'mag':magnitude,
'id':'ss3',
'rake':rake})
rup = rupture.QuadRupture.fromTrace(
np.array([tlon[0]]), np.array([tlat[0]]),
np.array([tlon[1]]), np.array([tlat[1]]),
zp, width, dip, origin, reference='ss3')
x = np.linspace(0, 20, 6)
y = np.linspace(0, 90, 11)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
deps = np.zeros_like(slon)
test1 = Bayless2013(origin, rup, slat, slon, deps, T=1.0)
phyp = copy.deepcopy(test1.phyp[0])
plat, plon, pdep = ecef2latlon(phyp.x, phyp.y, phyp.z)
px, py = proj(plon, plat, reverse=False)
np.testing.assert_allclose(plat, 38.004233219183604, rtol=1e-4)
np.testing.assert_allclose(plon, -120.98636122402166, rtol=1e-4)
np.testing.assert_allclose(pdep, 7.4999999989205968, rtol=1e-4)
#---------------------------------------------------------------------------
# Also for multiple segments
#---------------------------------------------------------------------------
dip = np.array([90., 90., 90.])
rake = 180.0
width = np.array([15., 15., 10.])
rupx = np.array([0., 0., 10., 20.])
rupy = np.array([0., 20., 60., 80.])
zp = np.array([0., 0., 0.])
epix = np.array([0.])
epiy = np.array([0.])
# Convert to lat/lon
proj = geo.utils.get_orthographic_projection(-122, -120, 39, 37)
tlon, tlat = proj(rupx, rupy, reverse=True)
epilon, epilat = proj(epix, epiy, reverse=True)
rup = rupture.QuadRupture.fromTrace(
np.array(tlon[0:3]), np.array(tlat[0:3]),
np.array(tlon[1:4]), np.array(tlat[1:4]),
zp, width, dip, origin, reference='')
event = {'lat': epilat[0],
'lon': epilon[0],
'depth': 1.0,
'mag': magnitude,
'id': '',
'locstring': 'test',
'type': 'SS',
'timezone': 'UTC'}
event['time'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
event['created'] = ShakeDateTime.utcfromtimestamp(int(time.time()))
x = np.linspace(0, 20, 6)
y = np.linspace(0, 90, 11)
site_x, site_y = np.meshgrid(x, y)
slon, slat = proj(site_x, site_y, reverse=True)
deps = np.zeros_like(slon)
origin = Origin(event)
origin.rake = rake
test1 = Bayless2013(origin, rup, slat, slon, deps, T=1.0)
# 1st pseudo-hyp
phyp = copy.deepcopy(test1.phyp[0])
plat, plon, pdep = ecef2latlon(phyp.x, phyp.y, phyp.z)
px, py = proj(plon, plat, reverse=False)
np.testing.assert_allclose(plat, 38.004233219183604, rtol=1e-4)
np.testing.assert_allclose(plon, -120.98636122402166, rtol=1e-4)
np.testing.assert_allclose(pdep, 7.4999999989205968, rtol=1e-4)
# 2nd pseudo-hyp
phyp = copy.deepcopy(test1.phyp[1])
plat, plon, pdep = ecef2latlon(phyp.x, phyp.y, phyp.z)
px, py = proj(plon, plat, reverse=False)
np.testing.assert_allclose(plat, 38.184097835787796, rtol=1e-4)
np.testing.assert_allclose(plon, -120.98636122402166, rtol=1e-4)
np.testing.assert_allclose(pdep, 7.4999999989103525, rtol=1e-4)
# 3rd pseudo-hyp
phyp = copy.deepcopy(test1.phyp[2])
plat, plon, pdep = ecef2latlon(phyp.x, phyp.y, phyp.z)
px, py = proj(plon, plat, reverse=False)
np.testing.assert_allclose(plat, 38.543778594535752, rtol=1e-4)
np.testing.assert_allclose(plon, -120.87137783362499, rtol=1e-4)
np.testing.assert_allclose(pdep, 4.9999999995063993, rtol=1e-4)
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')
