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_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_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')
