def test_fromTrace():
xp0 = [0.0]
xp1 = [0.0]
yp0 = [0.0]
yp1 = [0.05]
zp = [0.0]
widths = [10.0]
dips = [45.0]
# Rupture requires an origin even when not used:
origin = Origin({'id':'test','lat':0,'lon':0,'depth':5.0,'mag':7.0})
rupture = QuadRupture.fromTrace(
xp0, yp0, xp1, yp1, zp, widths,
dips, origin,
reference='From J Smith, (personal communication)')
fstr = io.StringIO()
rupture.writeTextFile(fstr)
xp0 = [-121.81529, -121.82298]
xp1 = [-121.82298, -121.83068]
yp0 = [37.73707, 37.74233]
yp1 = [37.74233, 37.74758]
zp = [10, 15]
widths = [15.0, 20.0]
dips = [30.0, 45.0]
rupture = QuadRupture.fromTrace(
xp0, yp0, xp1, yp1, zp, widths,
dips, origin,
reference='From J Smith, (personal communication)')
def test_fromTrace():
xp0 = [0.0]
xp1 = [0.0]
yp0 = [0.0]
yp1 = [0.05]
zp = [0.0]
widths = [10.0]
dips = [45.0]
rupture = QuadRupture.fromTrace(
xp0, yp0, xp1, yp1, zp, widths,
dips, reference='From J Smith, (personal communication)')
fstr = io.StringIO()
rupture.writeTextFile(fstr)
xp0 = [-121.81529, -121.82298]
xp1 = [-121.82298, -121.83068]
yp0 = [37.73707, 37.74233]
yp1 = [37.74233, 37.74758]
zp = [10, 15]
widths = [15.0, 20.0]
dips = [30.0, 45.0]
rupture = QuadRupture.fromTrace(
xp0, yp0, xp1, yp1, zp, widths,
dips, reference='From J Smith, (personal communication)')
def test_misc():
# Make a rupture
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 = QuadRupture.fromTrace(lon0, lat0, lon1, lat1, z, W, dip)
fm = flt.getRuptureAsMesh()
fa = flt.getRuptureAsArrays()
ref = flt.getReference()
def test_EdgeRupture_vs_QuadRupture():
# Sites stuff
sites = Sites.fromCenter(-122.15, 37.15, 1.5, 1.5, 0.01, 0.01)
sm_dict = sites._GeoDict
west = sm_dict.xmin
east = sm_dict.xmax
south = sm_dict.ymin
north = sm_dict.ymax
nx = sm_dict.nx
ny = sm_dict.ny
lats = np.linspace(north, south, ny)
lons = np.linspace(west, east, nx)
lon, lat = np.meshgrid(lons, lats)
dep = np.zeros_like(lon)
# Construct QuadRupture
xp0 = np.array([-122.0, -122.5])
yp0 = np.array([37.1, 37.4])
xp1 = np.array([-121.7, -122.3])
yp1 = np.array([37.2, 37.2])
zp = np.array([0, 6])
widths = np.array([30, 20])
dips = np.array([30, 40])
origin = Origin({"lat": 0, "lon": 0, "depth": 0, "mag": 7.2, "id": ""})
qrup = QuadRupture.fromTrace(xp0, yp0, xp1, yp1, zp, widths, dips, origin)
rrup_q = qrup.computeRrup(lon, lat, dep)
rjb_q = qrup.computeRjb(lon, lat, dep)
# Construct equivalent EdgeRupture
toplons = np.array([-122.0, -121.7, -122.5, -122.3])
toplats = np.array([37.1, 37.2, 37.4, 37.2])
topdeps = np.array([0, 0, 6, 6])
botlons = np.array([-121.886864, -121.587568, -122.635467, -122.435338])
botlats = np.array([36.884527, 36.984246, 37.314035, 37.114261])
botdeps = np.array([15.0000, 14.9998, 18.8558, 18.8559])
group_index = [0, 0, 1, 1]
erup = EdgeRupture.fromArrays(toplons, toplats, topdeps, botlons, botlats, botdeps, origin, group_index)
rrup_e = erup.computeRrup(lon, lat, dep)
rjb_e = erup.computeRjb(lon, lat, dep)
# Check that QuadRupture and EdgeRupture give the same result
# (we check the absolute values of QuadRupture elsewhere)
np.testing.assert_allclose(rrup_e, rrup_q, atol=0.35)
np.testing.assert_allclose(rjb_e, rjb_q, atol=0.35)
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.])
# Rupture requires an origin even when not used:
origin = Origin({'id':'test','lat':0,'lon':0,'depth':5.0,'mag':7.0})
rup = QuadRupture.fromTrace(lon0, lat0, lon1, lat1, z, W, dip,
origin)
event = {'lat': 34.1, 'lon': -118.2, 'depth': 1, 'mag': 6,
'id': '', 'locstring': '', 'type': 'U', 'mech':'RS',
'rake':90, 'timezone': 'UTC'}
origin = Origin(event)
gmpelist = ["Primate"]
with pytest.raises(Exception) as e:
dists = Distance.fromSites(gmpelist, origin, site, rup)
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), rup)
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), rup)
def test_slip():
# Make a rupture
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 = QuadRupture.fromTrace(lon0, lat0, lon1, lat1, z, W, dip)
slp = get_quad_slip(flt.getQuadrilaterals()[0], 30).getArray()
slpd = np.array([0.80816457, 0.25350787, 0.53160491])
np.testing.assert_allclose(slp, slpd)
slp = get_local_unit_slip_vector(22, 30, 86).getArray()
slpd = np.array([0.82714003, 0.38830563, 0.49878203])
np.testing.assert_allclose(slp, slpd)
def test_slip():
# Rupture requires an origin even when not used:
origin = Origin({'id':'test','lat':0,'lon':0,'depth':5.0,'mag':7.0})
# Make a rupture
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.])
rup = QuadRupture.fromTrace(lon0, lat0, lon1, lat1, z, W, dip, origin)
slp = get_quad_slip(rup.getQuadrilaterals()[0], 30).getArray()
slpd = np.array([0.80816457, 0.25350787, 0.53160491])
np.testing.assert_allclose(slp, slpd)
slp = get_local_unit_slip_vector(22, 30, 86).getArray()
slpd = np.array([0.82714003, 0.38830563, 0.49878203])
np.testing.assert_allclose(slp, slpd)
def test_chichi_with_get_distance():
# read in rupture 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)
# event information doesn't matter except hypocenter
event = {'lat': 23.85, 'lon': 120.82, 'depth': 8, 'mag': 7.62,
'id': '', 'locstring': '', 'type': 'ALL',
'timezone': 'UTC'}
origin = Origin(event)
rup = QuadRupture.fromVertices(
lon0, lat0, z0, lon1, lat1, z1, lon2, lat2, z2, lon3, lat3, z3,
origin)
# 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, rup)
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_distance_from_sites_origin():
# 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.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.])
event = {'lat': 34.1, 'lon': -118.2, 'depth': 1, 'mag': 6,
'id': '', 'locstring': '', 'type': 'ALL',
'timezone': 'UTC'}
origin = Origin(event)
rup = QuadRupture.fromTrace(lon0, lat0, lon1, lat1, z, W, dip, origin)
gmpelist = [AbrahamsonEtAl2014(), BergeThierryEtAl2003SIGMA()]
dists = Distance.fromSites(gmpelist, site, rup)
dctx = dists.getDistanceContext()
rhypo = np.array([[ 3.74498133, 3.32896405, 3.05225679, 2.95426722, 3.05225679,
3.32896405, 3.74498133],
[ 3.11965436, 2.60558436, 2.24124201, 2.10583262, 2.24124201,
2.60558436, 3.11965436],
[ 2.67523213, 2.05265767, 1.564393 , 1.36331682, 1.564393 ,
2.05265767, 2.67523213],
[ 2.50973226, 1.83166664, 1.26045653, 1. , 1.26045653,
1.83166664, 2.50973226],
[ 2.67542717, 2.05277065, 1.56443006, 1.36331682, 1.56443006,
2.05277065, 2.67542717],
[ 3.11998886, 2.60576236, 2.24129374, 2.10583262, 2.24129374,
2.60576236, 3.11998886],
[ 3.74539929, 3.32917303, 3.05231378, 2.95426722, 3.05231378,
3.32917303, 3.74539929]])
np.testing.assert_allclose(
rhypo, dctx.rhypo, rtol=0, atol=0.01)
rx = np.array([[ -3.18894050e+00, -2.48001769e+00, -1.77111874e+00,
-1.06224366e+00, -3.53392480e-01, 3.55434794e-01,
1.06423815e+00],
[ -2.83506890e+00, -2.12607622e+00, -1.41710740e+00,
-7.08162466e-01, 7.58576362e-04, 7.09655709e-01,
1.41852892e+00],
[ -2.48119723e+00, -1.77213470e+00, -1.06309603e+00,
-3.54081243e-01, 3.54909645e-01, 1.06387662e+00,
1.77281967e+00],
[ -2.12732550e+00, -1.41819312e+00, -7.09084619e-01,
2.56774082e-12, 7.09060719e-01, 1.41809752e+00,
2.12711040e+00],
[ -1.77345370e+00, -1.06425151e+00, -3.55073182e-01,
3.54081255e-01, 1.06321179e+00, 1.77231841e+00,
2.48140110e+00],
[ -1.41958186e+00, -7.10309855e-01, -1.06172493e-03,
7.08162516e-01, 1.41736285e+00, 2.12653927e+00,
2.83569175e+00],
[ -1.06570997e+00, -3.56368176e-01, 3.52949744e-01,
1.06224377e+00, 1.77151390e+00, 2.48076010e+00,
3.18998236e+00]])
np.testing.assert_allclose(
rx, dctx.rx, rtol=0, atol=0.01)
rjb = np.array([[ 3.19372137e+00, 2.48373511e+00, 1.77377308e+00,
1.06383562e+00, 3.53925643e-01, 2.25816823e-03,
2.45009861e-03],
[ 2.83931844e+00, 2.12926243e+00, 1.41923064e+00,
7.09223517e-01, 1.57594916e-03, 1.86044244e-03,
2.05239165e-03],
[ 2.48510934e+00, 1.77479025e+00, 1.06468863e+00,
3.54611655e-01, 1.04375185e-03, 1.32827303e-03,
1.52024106e-03],
[ 2.30690967e+00, 1.53793979e+00, 7.68969896e-01,
5.88918451e-12, 3.77111295e-04, 6.61660373e-04,
8.53647223e-04],
[ 2.48531877e+00, 1.79442084e+00, 1.20242597e+00,
8.54793253e-01, 5.62052963e-01, 2.69254693e-01,
5.26105100e-05],
[ 2.95646628e+00, 2.40489915e+00, 2.00231070e+00,
1.70958533e+00, 1.41681634e+00, 1.12398937e+00,
8.63761551e-01],
[ 3.60741953e+00, 3.17112489e+00, 2.85711592e+00,
2.56437623e+00, 2.27157856e+00, 1.97872291e+00,
1.78518260e+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],
[ 2.29490054e-02, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00],
[ 8.79341006e-01, 5.86285236e-01, 2.93171565e-01,
6.21003581e-12, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00],
[ 1.73573289e+00, 1.44264826e+00, 1.14950573e+00,
8.56305300e-01, 5.63046975e-01, 2.69730762e-01,
0.00000000e+00],
[ 2.59212463e+00, 2.29901116e+00, 2.00583977e+00,
1.71261048e+00, 1.41932329e+00, 1.12597821e+00,
8.32575235e-01],
[ 3.44851622e+00, 3.15537391e+00, 2.86217367e+00,
2.56891553e+00, 2.27559947e+00, 1.98222553e+00,
1.68879368e+00]])
np.testing.assert_allclose(
ry0, dctx.ry0, rtol=0, atol=0.01)
rrup = np.array([[ 3.34678672, 2.67788811, 2.03697073, 1.46129187, 1.06271102,
1.06352692, 1.40073832],
[ 3.01030105, 2.3526499 , 1.73673635, 1.22706347, 1.00157564,
1.22283363, 1.57764099],
[ 2.67858182, 2.03712377, 1.46095502, 1.06170931, 1.06220616,
1.39958479, 1.75442695],
[ 2.51415965, 1.8343632 , 1.26143652, 1. , 1.2212501 ,
1.57621925, 1.9310962 ],
[ 2.67877609, 2.05412785, 1.56384179, 1.3617346 , 1.50608502,
1.77308319, 2.10764873],
[ 3.12078859, 2.6043486 , 2.23799413, 2.09885629, 2.11696797,
2.23191013, 2.4299612 ],
[ 3.74318473, 3.32482368, 3.04635272, 2.9183523 , 2.86659485,
2.88815116, 2.98141559]])
np.testing.assert_allclose(
rrup, dctx.rrup, rtol=0, atol=0.01)
def test_san_fernando():
# This is a challenging rupture due to overlapping and discordant
# segments, as brought up by Graeme Weatherill. Our initial
# implementation put the origin on the wrong side of the rupture.
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)
# Rupture requires an origin even when not used:
origin = Origin({'id':'test','lat':0,'lon':0,'depth':5.0,'mag':7.0})
rup = QuadRupture.fromVertices(
lon0, lat0, z0, lon1, lat1, z1, lon2, lat2, z2, lon3, lat3, z3,
origin)
# Make a origin object; most of the 'event' values don't matter
event = {'lat': 0, 'lon': 0, 'depth':0, 'mag': 6.61,
'id':'', 'locstring':'', 'type':'ALL',
'timezone':'UTC'}
origin = Origin(event)
# Grid of sites
buf = 0.25
lat = np.linspace(np.nanmin(rup._lat)-buf, np.nanmax(rup._lat)+buf, 10)
lon = np.linspace(np.nanmin(rup._lon)-buf, np.nanmax(rup._lon)+buf, 10)
lons, lats = np.meshgrid(lon, lat)
dep = np.zeros_like(lons)
x,y = proj(lon, lat)
rupx,rupy = proj(rup._lon[~np.isnan(rup._lon)],
rup._lat[~np.isnan(rup._lat)])
# Calculate U and T
dtypes = ['U', 'T']
dists = get_distance(dtypes, lats, lons, dep, rup)
# new method:
from shakemap.grind.rupture import _computeGC2
ddict = _computeGC2(rup, lons, lats, dep)
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, atol = 0.01)
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, atol = 0.01)
def _test_intensity():
datadir = os.path.abspath(os.path.join(homedir, "..", "data", "eventdata", "northridge"))
shakefile = os.path.join(datadir, "northridge_grid.xml")
topofile = os.path.join(datadir, "northridge_topo.grd")
rupturefile = os.path.join(datadir, "northridge_fault.txt")
cityfile = os.path.join(datadir, "northridge_cities.txt")
coastfile = os.path.join(datadir, "northridge_coastline.json")
countryfile = os.path.join(datadir, "northridge_countries.json")
statefile = os.path.join(datadir, "northridge_states.json")
lakefile = os.path.join(datadir, "northridge_lakes.json")
oceanfile = os.path.join(datadir, "northridge_ocean.json")
stationfile = os.path.join(datadir, "northridge_stations.db")
roadfile = os.path.join(datadir, "northridge_roads.json")
tancptfile = os.path.join(shakedir, "shakemap", "mapping", "tan.cpt")
shakecptfile = os.path.join(shakedir, "shakemap", "mapping", "shakecpt.cpt")
layerdict = {
"coast": coastfile,
"ocean": oceanfile,
"lake": lakefile,
"country": countryfile,
"roads": roadfile,
"state": statefile,
}
tancolormap = ColorPalette.fromPreset("shaketopo")
shakecolormap = ColorPalette.fromPreset("mmi")
cities = BasemapCities.loadFromCSV(cityfile)
shakemap = ShakeGrid.load(shakefile, adjust="res")
stations = StationList(stationfile)
rupture = QuadRupture.readRuptureFile(rupturefile)
edict = shakemap.getEventDict()
eventdict = {
"lat": edict["lat"],
"lon": edict["lon"],
"depth": edict["depth"],
"mag": edict["magnitude"],
"time": edict["event_timestamp"],
}
source = Source(eventdict, rupture)
maker = MapMaker(shakemap, topofile, stations, rupture, layerdict, source, cities)
# draw intensity map
outfolder = os.path.expanduser("~")
maker.setIntensityLayer("mmi")
maker.setIntensityGMTColorMap(shakecolormap)
intensity_map = maker.drawIntensityMap(outfolder)
print("Intensity map saved as: %s" % intensity_map)
# draw contour maps
maker.setContourGMTColorMap(tancolormap)
# Draw pgv contours
maker.setContourLayer("pgv")
contour_pgv_map = maker.drawContourMap(outfolder)
print("PGV contour map saved as: %s" % contour_pgv_map)
# Draw pga contours
maker.setContourLayer("pga")
contour_pga_map = maker.drawContourMap(outfolder)
print("PGA contour map saved as: %s" % contour_pga_map)
# Draw psa0.3 contours
maker.setContourLayer("psa03")
contour_psa03_map = maker.drawContourMap(outfolder)
print("PSA0.3 contour map saved as: %s" % contour_psa03_map)
# Draw psa1.0 contours
maker.setContourLayer("psa10")
contour_psa10_map = maker.drawContourMap(outfolder)
print("PSA1.0 contour map saved as: %s" % contour_psa10_map)
# Draw psa3.0 contours
maker.setContourLayer("psa30")
contour_psa30_map = maker.drawContourMap(outfolder)
print("PSA3.0 contour map saved as: %s" % contour_psa30_map)
def _test_intensity():
datadir = os.path.abspath(os.path.join(
homedir, '..', 'data', 'eventdata', 'northridge'))
shakefile = os.path.join(datadir, 'northridge_grid.xml')
topofile = os.path.join(datadir, 'northridge_topo.grd')
rupturefile = os.path.join(datadir, 'northridge_fault.txt')
cityfile = os.path.join(datadir, 'northridge_cities.txt')
coastfile = os.path.join(datadir, 'northridge_coastline.json')
countryfile = os.path.join(datadir, 'northridge_countries.json')
statefile = os.path.join(datadir, 'northridge_states.json')
lakefile = os.path.join(datadir, 'northridge_lakes.json')
oceanfile = os.path.join(datadir, 'northridge_ocean.json')
stationfile = os.path.join(datadir, 'northridge_stations.db')
roadfile = os.path.join(datadir, 'northridge_roads.json')
tancptfile = os.path.join(shakedir, 'shakemap', 'mapping', 'tan.cpt')
shakecptfile = os.path.join(
shakedir, 'shakemap', 'mapping', 'shakecpt.cpt')
layerdict = {'coast': coastfile,
'ocean': oceanfile,
'lake': lakefile,
'country': countryfile,
'roads': roadfile,
'state': statefile}
tancolormap = ColorPalette.fromPreset('shaketopo')
shakecolormap = ColorPalette.fromPreset('mmi')
cities = BasemapCities.loadFromCSV(cityfile)
shakemap = ShakeGrid.load(shakefile, adjust='res')
stations = StationList(stationfile)
rupture = QuadRupture.readRuptureFile(rupturefile)
edict = shakemap.getEventDict()
eventdict = {'lat': edict['lat'],
'lon': edict['lon'],
'depth': edict['depth'],
'mag': edict['magnitude'],
'time': edict['event_timestamp']}
origin = Origin(eventdict)
maker = MapMaker(shakemap, topofile, stations,
rupture, layerdict, origin, cities)
# draw intensity map
outfolder = os.path.expanduser('~')
maker.setIntensityLayer('mmi')
maker.setIntensityGMTColorMap(shakecolormap)
intensity_map = maker.drawIntensityMap(outfolder)
print('Intensity map saved as: %s' % intensity_map)
# draw contour maps
maker.setContourGMTColorMap(tancolormap)
# Draw pgv contours
maker.setContourLayer('pgv')
contour_pgv_map = maker.drawContourMap(outfolder)
print('PGV contour map saved as: %s' % contour_pgv_map)
# Draw pga contours
maker.setContourLayer('pga')
contour_pga_map = maker.drawContourMap(outfolder)
print('PGA contour map saved as: %s' % contour_pga_map)
# Draw psa0.3 contours
maker.setContourLayer('psa03')
contour_psa03_map = maker.drawContourMap(outfolder)
print('PSA0.3 contour map saved as: %s' % contour_psa03_map)
# Draw psa1.0 contours
maker.setContourLayer('psa10')
contour_psa10_map = maker.drawContourMap(outfolder)
print('PSA1.0 contour map saved as: %s' % contour_psa10_map)
# Draw psa3.0 contours
maker.setContourLayer('psa30')
contour_psa30_map = maker.drawContourMap(outfolder)
print('PSA3.0 contour map saved as: %s' % contour_psa30_map)
def test_multisegment_discordant():
# The one thing that isn't check above is discordancy for segments
# with multiple quads. For this, we need a synthetic example.
x0 = np.array([0, 1, -1, 10, 9, 7])
y0 = np.array([0, 10, 20, 40, 35, 30])
z0 = np.array([0, 0, 0, 0, 0, 0])
x1 = np.array([1, -1, 0, 9, 7, 6])
y1 = np.array([10, 20, 30, 35, 30, 25])
z1 = np.array([0, 0, 0, 0, 0, 0])
x2 = np.array([3, 1, 2, 7, 5, 4])
y2 = np.array([10, 20, 30, 35, 30, 25])
z2 = np.array([10, 10, 10, 10, 10, 10])
x3 = np.array([2, 3, 1, 8, 7, 5])
y3 = np.array([0, 10, 20, 40, 35, 30])
z3 = np.array([10, 10, 10, 10, 10, 10])
epilat = 32.15270
epilon = -115.30500
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)
# Make an Origin object; most of the 'event' values don't matter for this example
origin = Origin({"lat": 0, "lon": 0, "depth": 0, "mag": 7.2, "id": ""})
rup = QuadRupture.fromVertices(
lon0, lat0, z0, lon1, lat1, z1, lon2, lat2, z2, lon3, lat3, z3, origin, group_index=[0, 0, 0, 1, 1, 1]
)
# Sites
buf = 0.25
lat = np.linspace(np.nanmin(rup.lats) - buf, np.nanmax(rup.lats) + buf, 20)
lon = np.linspace(np.nanmin(rup.lons) - buf, np.nanmax(rup.lons) + buf, 20)
lons, lats = np.meshgrid(lon, lat)
dep = np.zeros_like(lons)
x, y = proj(lon, lat)
rupx, rupy = proj(rup.lons, rup.lats)
# Calculate U and T
dtypes = ["U", "T"]
dists = get_distance(dtypes, lats, lons, dep, rup)
targetU = np.array(
[
[
-28.53228275,
-28.36479713,
-28.20139732,
-28.0407734,
-27.88135558,
-27.72144153,
-27.55935946,
-27.39362017,
-27.22300147,
-27.04653062,
-26.86338215,
-26.67275638,
-26.47381287,
-26.26569449,
-26.04762427,
-25.81902477,
-25.57961136,
-25.32943282,
-25.06885791,
-24.79852214,
],
[
-23.53750292,
-23.3748086,
-23.21793537,
-23.06521934,
-22.91449689,
-22.76331684,
-22.60928211,
-22.45042208,
-22.28542121,
-22.11355532,
-21.93435402,
-21.74720475,
-21.55115107,
-21.34497916,
-21.12749377,
-20.89781118,
-20.6555466,
-20.40086149,
-20.13439948,
-19.85716145,
],
[
-18.53499939,
-18.37689929,
-18.22732841,
-18.08427516,
-17.94468687,
-17.80472632,
-17.66045115,
-17.50880802,
-17.3484421,
-17.17963435,
-17.0032098,
-16.81921732,
-16.62638972,
-16.42258419,
-16.20564846,
-15.9741218,
-15.72753538,
-15.4663671,
-15.19180844,
-14.9054813,
],
[
-13.52283359,
-13.36797542,
-13.22589288,
-13.09466537,
-12.97028551,
-12.84653536,
-12.71591089,
-12.57212088,
-12.41335561,
-12.24319318,
-12.06681006,
-11.88598424,
-11.69798166,
-11.49796348,
-11.28169605,
-11.04691388,
-10.79343174,
-10.52262594,
-10.23677602,
-9.93851158,
],
[
-8.49936685,
-8.34357094,
-8.20650964,
-8.08786858,
-7.98403171,
-7.88628837,
-7.78005273,
-7.64833307,
-7.48359988,
-7.29992491,
-7.11862682,
-6.94410189,
-6.76618701,
-6.5727842,
-6.35634881,
-6.11465447,
-5.84925708,
-5.56369035,
-5.26212482,
-4.94857454,
],
[
-3.46638168,
-3.30047216,
-3.15914418,
-3.04618465,
-2.96252939,
-2.90194067,
-2.84436315,
-2.75029014,
-2.56983592,
-2.33744275,
-2.1512136,
-1.99833104,
-1.84066354,
-1.6541107,
-1.43071517,
-1.17252753,
-0.88592286,
-0.57817222,
-0.25582315,
0.07585567,
],
[
1.56416954,
1.75393848,
1.9183586,
2.04909316,
2.13723278,
2.17776584,
2.18272501,
2.20967639,
2.37405656,
2.65073289,
2.80205222,
2.90973407,
3.05124404,
3.2505182,
3.50336116,
3.7967575,
4.11742779,
4.45465822,
4.80070204,
5.15033407,
],
[
6.5633489,
6.78740885,
6.99419348,
7.17551069,
7.31963558,
7.4113505,
7.43666779,
7.40177458,
7.40517136,
7.58520044,
7.62013169,
7.71596777,
7.90558457,
8.17213015,
8.49008681,
8.83763176,
9.19937294,
9.56556659,
9.9305469,
10.29132309,
],
[
11.48996073,
11.74301446,
11.99016964,
12.22782156,
12.44984059,
12.6446727,
12.78798484,
12.82584849,
12.61992833,
12.26579742,
12.32166685,
12.54665462,
12.86628045,
13.23578462,
13.62571822,
14.01882924,
14.40617707,
14.78388296,
15.15089889,
15.5076165,
],
[
16.31383216,
16.57376544,
16.83189511,
17.08626411,
17.33309437,
17.56429108,
17.76005623,
17.85853532,
17.57101025,
17.32637346,
17.45075419,
17.77199513,
18.16933168,
18.58284635,
18.9891851,
19.37985879,
19.75324557,
20.11079653,
20.4549905,
20.78837053,
],
[
21.03975749,
21.28450315,
21.5243142,
21.75603974,
21.97469496,
22.17298057,
22.34310053,
22.49668569,
22.73940191,
22.70030633,
22.95351405,
23.35967832,
23.75891016,
24.14867803,
24.51536915,
24.85878249,
25.18398203,
25.49615514,
25.79932964,
26.09638269,
],
[
25.70484089,
25.92709225,
26.14280395,
26.35119497,
26.55363501,
26.75827099,
26.9915523,
27.31779086,
27.77993211,
27.71070831,
28.13624949,
28.723482,
29.25285078,
29.66404032,
30.00169474,
30.30044315,
30.57916576,
30.84804427,
31.1126134,
31.37586841,
],
[
30.35406633,
30.5585145,
30.75843356,
30.95627127,
31.15811912,
31.3763124,
31.63114968,
31.94156189,
32.23691802,
32.38759301,
32.86915665,
33.83467935,
34.46125278,
34.89905345,
35.25111257,
35.55095664,
35.82150686,
36.07720619,
36.32643896,
36.57385362,
],
[
35.0222379,
35.21734711,
35.41081942,
35.60589495,
35.80774808,
36.02313791,
36.25826988,
36.51619168,
36.81025966,
37.21777129,
37.86674108,
38.66578072,
39.25203723,
39.78060643,
40.20815617,
40.5606039,
40.86634527,
41.14457482,
41.40732554,
41.66197722,
],
[
39.73046099,
39.92514041,
40.12152415,
40.32316112,
40.5350467,
40.76393316,
41.01937758,
41.3172128,
41.68596492,
42.16604148,
42.77622755,
43.447503,
44.03771478,
44.55012468,
45.00551259,
45.40376857,
45.75505135,
46.07204699,
46.36554362,
46.64361367,
],
[
44.4876174,
44.68959464,
44.89710008,
45.11420443,
45.34646809,
45.60143197,
45.88932906,
46.22363997,
46.61975585,
47.0884227,
47.62307543,
48.1913408,
48.74937117,
49.26945799,
49.74327902,
50.17123158,
50.55810895,
50.91098842,
51.23731582,
51.54375617,
],
[
49.29279265,
49.50696882,
49.73006999,
49.96625305,
50.22080319,
50.50022572,
50.81209441,
51.1642666,
51.56290694,
52.00913021,
52.49553006,
53.00565389,
53.51861282,
54.01614414,
54.48672101,
54.9254339,
55.33212663,
55.70951516,
56.06170563,
56.39317058,
],
[
54.13906629,
54.3671694,
54.60643024,
54.86053563,
55.13377911,
55.43088558,
55.75658576,
56.1148189,
56.50752978,
56.93329478,
57.38640012,
57.85715119,
58.33367994,
58.80451404,
59.26065475,
59.69644542,
60.10938419,
60.49940252,
60.86803179,
61.21767916,
],
[
59.01741908,
59.25887491,
59.51248349,
59.78119592,
60.06816694,
60.37651862,
60.70895927,
61.0672529,
61.45160192,
61.86010542,
62.28853397,
62.73062937,
63.17894547,
63.62598375,
64.06523791,
64.49185106,
64.90281064,
65.2967858,
65.67377362,
66.03469546,
],
[
63.9193099,
64.17236414,
64.4376317,
64.71732366,
65.01362255,
65.32847988,
65.66334836,
66.0188704,
66.39457546,
66.7886684,
67.19800022,
67.61828012,
68.04451487,
68.47157851,
68.89476917,
69.31022713,
69.71515194,
70.10782673,
70.4875021,
70.85420436,
],
]
)
np.testing.assert_allclose(targetU, dists["U"], atol=0.01)
targetT = np.array(
[
[
-2.27427469e01,
-1.97498544e01,
-1.67512900e01,
-1.37464632e01,
-1.07350712e01,
-7.71715083e00,
-4.69305811e00,
-1.66336318e00,
1.37131605e00,
4.41047613e00,
7.45381136e00,
1.05011799e01,
1.35524779e01,
1.66074913e01,
1.96657949e01,
2.27267294e01,
2.57894503e01,
2.88530154e01,
3.19164798e01,
3.49789747e01,
],
[
-2.30778766e01,
-2.00896906e01,
-1.70950973e01,
-1.40931667e01,
-1.10834219e01,
-8.06600712e00,
-5.04171582e00,
-2.01179123e00,
1.02248614e00,
4.06025218e00,
7.10129626e00,
1.01459367e01,
1.31946312e01,
1.62475702e01,
1.93044511e01,
2.23644788e01,
2.54265185e01,
2.84892997e01,
3.15515954e01,
3.46123426e01,
],
[
-2.33971472e01,
-2.04144525e01,
-1.74245193e01,
-1.44256870e01,
-1.14169177e01,
-8.39830615e00,
-5.37141115e00,
-2.33902937e00,
6.95823925e-01,
3.73133431e00,
6.76769593e00,
9.80663091e00,
1.28500821e01,
1.58991008e01,
1.89534737e01,
2.20119662e01,
2.50728111e01,
2.81341606e01,
3.11943854e01,
3.42522163e01,
],
[
-2.36965870e01,
-2.07206976e01,
-1.77370901e01,
-1.47426715e01,
-1.17347885e01,
-8.71247709e00,
-5.67801094e00,
-2.63761285e00,
4.00625914e-01,
3.43182302e00,
6.45782532e00,
9.48491128e00,
1.25187545e01,
1.55616657e01,
1.86127822e01,
2.16694756e01,
2.47286680e01,
2.77876297e01,
3.08443066e01,
3.38973527e01,
],
[
-2.39698399e01,
-2.10022612e01,
-1.80281475e01,
-1.50423801e01,
-1.20388157e01,
-9.01204040e00,
-5.96160398e00,
-2.89867328e00,
1.52194374e-01,
3.17268218e00,
6.17334725e00,
9.17699572e00,
1.21964990e01,
1.52330975e01,
1.82821226e01,
2.13375815e01,
2.43943933e01,
2.74490375e01,
3.04994435e01,
3.35446330e01,
],
[
-2.42070742e01,
-2.12471979e01,
-1.82855675e01,
-1.53163304e01,
-1.23296744e01,
-9.31127857e00,
-6.24535210e00,
-3.12882361e00,
-2.24460581e-02,
2.95354485e00,
5.89215412e00,
8.86387424e00,
1.18748249e01,
1.49128245e01,
1.79640055e01,
2.10182501e01,
2.40696313e01,
2.71153177e01,
3.01543919e01,
3.31869788e01,
],
[
-2.43971375e01,
-2.14368866e01,
-1.84826148e01,
-1.55321207e01,
-1.25786621e01,
-9.60654678e00,
-6.58612151e00,
-3.48118311e00,
-3.16555025e-01,
2.61618307e00,
5.53740540e00,
8.52666510e00,
1.15623361e01,
1.46149780e01,
1.76674294e01,
2.07125025e01,
2.37483764e01,
2.67756033e01,
2.97955606e01,
3.28097430e01,
],
[
-2.45384925e01,
-2.15583842e01,
-1.85874288e01,
-1.56290738e01,
-1.26867853e01,
-9.76140655e00,
-6.84407754e00,
-3.90089971e00,
-8.41806596e-01,
2.14754495e00,
5.18583472e00,
8.26271822e00,
1.13266091e01,
1.43684333e01,
1.73916223e01,
2.04017469e01,
2.34034936e01,
2.64002111e01,
2.93941282e01,
3.23866586e01,
],
[
-2.46576775e01,
-2.16355610e01,
-1.86129545e01,
-1.55919156e01,
-1.25763765e01,
-9.57306672e00,
-6.59044329e00,
-3.62352541e00,
-5.92041388e-01,
2.33255341e00,
5.29498494e00,
8.24834463e00,
1.11833819e01,
1.41167617e01,
1.70571082e01,
2.00065102e01,
2.29645946e01,
2.59302937e01,
2.89023967e01,
3.18797332e01,
],
[
-2.48161623e01,
-2.17489533e01,
-1.86651328e01,
-1.55589864e01,
-1.24224388e01,
-9.24466730e00,
-6.01521475e00,
-2.75148770e00,
3.89519039e-01,
2.99589525e00,
5.45696689e00,
8.01247078e00,
1.07291540e01,
1.35565782e01,
1.64461360e01,
1.93723515e01,
2.23222250e01,
2.52881249e01,
2.82650171e01,
3.12494172e01,
],
[
-2.50857405e01,
-2.20002811e01,
-1.88926336e01,
-1.57550887e01,
-1.25770789e01,
-9.34497451e00,
-6.04430316e00,
-2.67290100e00,
5.40854953e-01,
2.30509492e00,
3.58183843e00,
6.23701436e00,
9.28727128e00,
1.23205706e01,
1.53428945e01,
1.83666035e01,
2.13934954e01,
2.44218171e01,
2.74496472e01,
3.04757209e01,
],
[
-2.55082697e01,
-2.24454912e01,
-1.93710045e01,
-1.62824768e01,
-1.31767102e01,
-1.00469827e01,
-6.86985653e00,
-3.54681638e00,
1.07062999e-01,
3.34891657e-01,
-1.70694750e-01,
3.57896940e00,
7.17013928e00,
1.05232789e01,
1.37976070e01,
1.70230221e01,
2.02076136e01,
2.33576919e01,
2.64794914e01,
2.95785985e01,
],
[
-2.60778515e01,
-2.30695744e01,
-2.00684150e01,
-1.70790651e01,
-1.41074315e01,
-1.11587507e01,
-8.23273307e00,
-5.33306966e00,
-2.80144302e00,
-1.84760416e00,
-1.05368779e00,
1.26163211e00,
4.90086292e00,
8.53883059e00,
1.20996577e01,
1.55589098e01,
1.89237978e01,
2.22114952e01,
2.54390313e01,
2.86203790e01,
],
[
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)
np.testing.assert_allclose(targetT, dists["T"], atol=0.01)
