class TaupGeoTestCase(unittest.TestCase):
"""
Test suite for the SeismicPhase class.
"""
def setUp(self):
self.model = TauPyModel('iasp91')
@unittest.skipIf(not geodetics.GEOGRAPHICLIB_VERSION_AT_LEAST_1_34,
'Module geographiclib is not installed or too old.')
def test_path_geo(self):
evlat, evlon = 0., 20.
evdepth = 10.
stlat, stlon = 0., -80.
arrivals = self.model.get_ray_paths_geo(evdepth, evlat, evlon, stlat,
stlon)
for arr in arrivals:
stlat_path = arr.path['lat'][-1]
stlon_path = arr.path['lon'][-1]
self.assertAlmostEqual(stlat, stlat_path, delta=0.1)
self.assertAlmostEqual(stlon, stlon_path, delta=0.1)
class TaupGeoTestCase(unittest.TestCase):
"""
Test suite for the SeismicPhase class.
"""
def setUp(self):
self.model = TauPyModel('iasp91')
@unittest.skipIf(not geodetics.GEOGRAPHICLIB_VERSION_AT_LEAST_1_34,
'Module geographiclib is not installed or too old.')
def test_path_geo(self):
evlat, evlon = 0., 20.
evdepth = 10.
stlat, stlon = 0., -80.
arrivals = self.model.get_ray_paths_geo(evdepth, evlat, evlon, stlat,
stlon)
for arr in arrivals:
stlat_path = arr.path['lat'][-1]
stlon_path = arr.path['lon'][-1]
self.assertAlmostEqual(stlat, stlat_path, delta=0.1)
self.assertAlmostEqual(stlon, stlon_path, delta=0.1)
@unittest.skipIf(not geodetics.GEOGRAPHICLIB_VERSION_AT_LEAST_1_34,
'Module geographiclib is not installed or too old.')
def test_path_resampling(self):
"""
Test resampling of paths in regions that are pretty much straight in
geographical coordinates and therefore only coarsely sampled by taup
"""
kwargs = dict(source_depth_in_km=50, source_latitude_in_deg=0.,
source_longitude_in_deg=0., receiver_latitude_in_deg=0.,
receiver_longitude_in_deg=150, phase_list=('Pdiff', ))
model = self.model
default = model.get_ray_paths_geo(resample=False, **kwargs)[0].path
resampled = model.get_ray_paths_geo(resample=True, **kwargs)[0].path
self.assertEqual(len(default), 397)
self.assertEqual(len(resampled), 416)
# start and end of path are unchanged
expected = [
(254.3313, 0., 0.00000000e+00, 50., 0., 0.),
(254.3313, 0.122393, 5.03916e-05, 50.931307, 0., 0.00288723),
(254.3313, 0.820524, 3.38136e-04, 56.243394, 0., 0.01937378),
(254.3313, 2.217176, 9.15375e-04, 66.870045, 0., 0.05244716),
(254.3313, 3.614348, 1.49496e-03, 77.5, 0., 0.08565492)]
np.testing.assert_allclose(default[:5].tolist(), expected, rtol=1e-5)
np.testing.assert_allclose(resampled[:5].tolist(), expected, rtol=1e-5)
expected = [(254.33137581441554, 1040.4579085173295, 2.617806788150977,
5.004051595465171, 0.0, 149.98928054174857),
(254.33137581441554, 1040.9008781834862,
2.6179002698779987, 2.5047268613761844, 0.0,
149.99463665016856),
(254.33137581441554, 1041.1223593199215,
2.6179469824519237, 1.2550644943312363, 0.0,
149.99731308350465),
(254.33137581441554, 1041.3438379939714, 2.617993676181489,
0.005402127286288305, 0.0, 149.99998843713843),
(254.33137581441554, 1041.3447954120252, 2.617993877991496,
0.0, 0.0, 150.0000000000001)]
np.testing.assert_allclose(default[-5:].tolist(), expected, rtol=1e-5)
np.testing.assert_allclose(resampled[-5:].tolist(), expected,
rtol=1e-5)
# in the middle the paths differ
expected_resampled = [
(254.33137581441554, 602.8221186084121, 94.33432576321066, 2889.0,
0.0, 94.33432576321066),
(254.33137581441554, 617.1726077606227, 97.56720436396148, 2889.0,
0.0, 97.56720436396148),
(254.33137581441554, 631.5230969128334, 1.759293334017677, 2889.0,
0.0, 100.80008296471232),
(254.33137581441554, 643.4514604207692, 103.4785176520484,
2880.9095668072578, 0.0, 103.4785176520484),
(254.33137581441554, 655.379823928705, 1.8527883422049563,
2872.8191336145155, 0.0, 106.15695233938449),
(254.33137581441554, 664.4057532502989, 108.1520313009955,
2856.0745668072577, 0.0, 108.1520313009955),
(254.33137581441554, 673.4316825718927, 1.9224297356397198,
2839.33, 0.0, 110.14711026260652),
(254.33137581441554, 688.0227541830799, 1.9774741001400498,
2799.2895131996584, 0.0, 113.30092003445516),
(254.33137581441554, 691.0197687282376, 1.9886081210291868,
2789.67, 0.0, 113.93885244041321),
(254.33137581441554, 692.3359202082614, 1.9934773547795543,
2785.297917906318, 0.0, 114.21783898377193)]
expected_default = [
(254.33137581441554, 740.555911561264, 2.160089140534382,
2560.645140025988, 0.0, 123.76399112466144),
(254.33137581441554, 743.6578548789879, 2.1698423363329122, 2542.0,
0.0, 124.32280808068195),
(254.33137581441554, 751.5598436136655, 2.194109696034333, 2492.5,
0.0, 125.71322537149922),
(254.33137581441554, 752.3706892487971, 2.196552592897761,
2487.2632826174126, 0.0, 125.8531930515594),
(254.33137581441554, 753.7391549563343, 2.200655453715524,
2478.3607118178234, 0.0, 126.08826966034681),
(254.33137581441554, 759.0587464034165, 2.2163649120816844, 2443.0,
0.0, 126.98835532316427),
(254.33137581441554, 763.4244896464312, 2.2289727423907655,
2413.1126874221954, 0.0, 127.71073078869175),
(254.33137581441554, 766.2313256113371, 2.2369430801261303, 2393.5,
0.0, 128.167397502222),
(254.33137581441554, 773.1340678380197, 2.256093920089646, 2344.0,
0.0, 129.26465980626193),
(254.33137581441554, 773.9286888315412, 2.258257579541221,
2338.1899362389727, 0.0, 129.38862836114077)]
np.testing.assert_allclose(default[205:215].tolist(), expected_default,
rtol=1e-5)
np.testing.assert_allclose(resampled[205:215].tolist(),
expected_resampled, rtol=1e-5)
class TaupGeoTestCase(unittest.TestCase):
"""
Test suite for the SeismicPhase class.
"""
def setUp(self):
self.model = TauPyModel('iasp91')
@unittest.skipIf(not geodetics.GEOGRAPHICLIB_VERSION_AT_LEAST_1_34,
'Module geographiclib is not installed or too old.')
def test_path_geo(self):
evlat, evlon = 0., 20.
evdepth = 10.
stlat, stlon = 0., -80.
arrivals = self.model.get_ray_paths_geo(evdepth, evlat, evlon, stlat,
stlon)
for arr in arrivals:
stlat_path = arr.path['lat'][-1]
stlon_path = arr.path['lon'][-1]
self.assertAlmostEqual(stlat, stlat_path, delta=0.1)
self.assertAlmostEqual(stlon, stlon_path, delta=0.1)
@unittest.skipIf(not geodetics.GEOGRAPHICLIB_VERSION_AT_LEAST_1_34,
'Module geographiclib is not installed or too old.')
def test_path_resampling(self):
"""
Test resampling of paths in regions that are pretty much straight in
geographical coordinates and therefore only coarsely sampled by taup
"""
kwargs = dict(source_depth_in_km=50,
source_latitude_in_deg=0.,
source_longitude_in_deg=0.,
receiver_latitude_in_deg=0.,
receiver_longitude_in_deg=150,
phase_list=('Pdiff', ))
model = self.model
default = model.get_ray_paths_geo(resample=False, **kwargs)[0].path
resampled = model.get_ray_paths_geo(resample=True, **kwargs)[0].path
self.assertEqual(len(default), 397)
self.assertEqual(len(resampled), 416)
# start and end of path are unchanged
expected = [
(254.3313, 0., 0.00000000e+00, 50., 0., 0.),
(254.3313, 0.122393, 5.03916e-05, 50.931307, 0., 0.00288723),
(254.3313, 0.820524, 3.38136e-04, 56.243394, 0., 0.01937378),
(254.3313, 2.217176, 9.15375e-04, 66.870045, 0., 0.05244716),
(254.3313, 3.614348, 1.49496e-03, 77.5, 0., 0.08565492)
]
np.testing.assert_allclose(default[:5].tolist(), expected, rtol=1e-5)
np.testing.assert_allclose(resampled[:5].tolist(), expected, rtol=1e-5)
expected = [
(254.33137581441554, 1040.4579085173295, 2.617806788150977,
5.004051595465171, 0.0, 149.98928054174857),
(254.33137581441554, 1040.9008781834862, 2.6179002698779987,
2.5047268613761844, 0.0, 149.99463665016856),
(254.33137581441554, 1041.1223593199215, 2.6179469824519237,
1.2550644943312363, 0.0, 149.99731308350465),
(254.33137581441554, 1041.3438379939714, 2.617993676181489,
0.005402127286288305, 0.0, 149.99998843713843),
(254.33137581441554, 1041.3447954120252, 2.617993877991496, 0.0,
0.0, 150.0000000000001)
]
np.testing.assert_allclose(default[-5:].tolist(), expected, rtol=1e-5)
np.testing.assert_allclose(resampled[-5:].tolist(),
expected,
rtol=1e-5)
# in the middle the paths differ
expected_resampled = [
(254.33137581441554, 602.8221186084121, 94.33432576321066, 2889.0,
0.0, 94.33432576321066),
(254.33137581441554, 617.1726077606227, 97.56720436396148, 2889.0,
0.0, 97.56720436396148),
(254.33137581441554, 631.5230969128334, 1.759293334017677, 2889.0,
0.0, 100.80008296471232),
(254.33137581441554, 643.4514604207692, 103.4785176520484,
2880.9095668072578, 0.0, 103.4785176520484),
(254.33137581441554, 655.379823928705, 1.8527883422049563,
2872.8191336145155, 0.0, 106.15695233938449),
(254.33137581441554, 664.4057532502989, 108.1520313009955,
2856.0745668072577, 0.0, 108.1520313009955),
(254.33137581441554, 673.4316825718927, 1.9224297356397198,
2839.33, 0.0, 110.14711026260652),
(254.33137581441554, 688.0227541830799, 1.9774741001400498,
2799.2895131996584, 0.0, 113.30092003445516),
(254.33137581441554, 691.0197687282376, 1.9886081210291868,
2789.67, 0.0, 113.93885244041321),
(254.33137581441554, 692.3359202082614, 1.9934773547795543,
2785.297917906318, 0.0, 114.21783898377193)
]
expected_default = [
(254.33137581441554, 740.555911561264, 2.160089140534382,
2560.645140025988, 0.0, 123.76399112466144),
(254.33137581441554, 743.6578548789879, 2.1698423363329122, 2542.0,
0.0, 124.32280808068195),
(254.33137581441554, 751.5598436136655, 2.194109696034333, 2492.5,
0.0, 125.71322537149922),
(254.33137581441554, 752.3706892487971, 2.196552592897761,
2487.2632826174126, 0.0, 125.8531930515594),
(254.33137581441554, 753.7391549563343, 2.200655453715524,
2478.3607118178234, 0.0, 126.08826966034681),
(254.33137581441554, 759.0587464034165, 2.2163649120816844, 2443.0,
0.0, 126.98835532316427),
(254.33137581441554, 763.4244896464312, 2.2289727423907655,
2413.1126874221954, 0.0, 127.71073078869175),
(254.33137581441554, 766.2313256113371, 2.2369430801261303, 2393.5,
0.0, 128.167397502222),
(254.33137581441554, 773.1340678380197, 2.256093920089646, 2344.0,
0.0, 129.26465980626193),
(254.33137581441554, 773.9286888315412, 2.258257579541221,
2338.1899362389727, 0.0, 129.38862836114077)
]
np.testing.assert_allclose(default[205:215].tolist(),
expected_default,
rtol=1e-5)
np.testing.assert_allclose(resampled[205:215].tolist(),
expected_resampled,
rtol=1e-5)
