/
test_ray_paths.py
181 lines (171 loc) · 8.65 KB
/
test_ray_paths.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
# -*- coding: utf-8 -*-
import os
import numpy as np
import pytest
import obspy
import obspy.geodetics.base as geodetics
from obspy.taup.ray_paths import get_ray_paths
from obspy.taup import TauPyModel
class TestRayPathCalculations:
"""
Test suite for obspy.taup.ray_paths
"""
def setUp(self):
# load an inventory and an event catalog to test
# the ray path routines. Careful, the full catalog
# test is quite long and is therefore commented out
# by default
self.path = os.path.join(os.path.dirname(__file__), 'images')
pass
@pytest.mark.skipif(
not geodetics.GEOGRAPHICLIB_VERSION_AT_LEAST_1_34,
reason='test needs geographiclib >= 1.34')
def test_compute_ray_paths(self):
# careful, the full inventory, catalog test is long (1min)
# greatcircles = get_ray_paths(
# inventory=self.inventory, catalog=self.catalog,
# phase_list=['P'], coordinate_system='XYZ',
# taup_model='iasp91')
# this test checks if we get a single P wave greatcircle
station = obspy.core.inventory.Station(
code='STA', latitude=0., longitude=30., elevation=0.)
# make two (identical) stations
network = obspy.core.inventory.Network(
code='NET', stations=[station, station])
inventory = obspy.core.inventory.Inventory(
source='ME', networks=[network])
otime = obspy.UTCDateTime('2017-02-03T12:00:00.0Z')
origin = obspy.core.event.Origin(latitude=0., longitude=90.,
depth=100000., time=otime)
origin.resource_id = 'smi:local/just-a-test2'
magnitude = obspy.core.event.Magnitude(mag=7.)
event = obspy.core.event.Event(origins=[origin],
magnitudes=[magnitude])
event.resource_id = 'smi:local/just-a-test'
# make three (identical) events
catalog = obspy.core.event.Catalog(events=[event, event, event])
# query for four phases
greatcircles = get_ray_paths(
inventory, catalog, phase_list=['P', 'PP', 'S', 'SS'],
coordinate_system='XYZ', taup_model='iasp91')
# two stations, three events, 4 phases should yield 24 rays (since all
# arrivals are encountered in this case)
assert len(greatcircles) == 24
# now check details of first ray
circ = greatcircles[0]
path = circ[0]
assert circ[1] == 'P'
assert circ[2] == 'NET.STA'
np.testing.assert_allclose(circ[3], otime.timestamp, atol=1e-5, rtol=0)
assert circ[4] == 7.0
assert circ[5] == 'smi:local/just-a-test'
assert circ[6] == 'smi:local/just-a-test2'
assert path.shape == (3, 274)
# now check some coordinates of the calculated path, start, end and
# some values in between
path_start_expected = [
[6.02712296e-17, 4.63135005e-05, 1.82928142e-03, 1.99453947e-03,
2.16015881e-03],
[9.84303877e-01, 9.84224340e-01, 9.81162947e-01, 9.80879369e-01,
9.80595408e-01],
[6.02712296e-17, 6.02663595e-17, 6.00790075e-17, 6.00616631e-17,
6.00442971e-17]]
np.testing.assert_allclose(path[:, :5], path_start_expected, rtol=1e-5)
path_end_expected = [
[8.65195410e-01, 8.65610142e-01, 8.65817499e-01,
8.66024850e-01, 8.66025746e-01],
[4.99866617e-01, 4.99932942e-01, 4.99966104e-01,
4.99999264e-01, 4.99999407e-01],
[6.11842455e-17, 6.12082668e-17, 6.12202774e-17,
6.12322880e-17, 6.12323400e-17]]
np.testing.assert_allclose(path[:, -5:], path_end_expected, rtol=1e-5)
path_steps_expected = [
[6.02712296e-17, 5.99694796e-03, 1.55844904e-02,
2.29391617e-02, 3.12959401e-02, 4.94819381e-02,
6.59026261e-02, 8.84601669e-02, 1.15734196e-01,
1.30670566e-01, 1.83202229e-01, 2.21387617e-01,
3.00609265e-01, 4.51339383e-01, 5.39194024e-01,
5.84050335e-01, 6.48856399e-01, 6.68018760e-01,
7.03962600e-01, 7.34809690e-01, 7.59887900e-01,
7.89619836e-01, 8.04521516e-01, 8.18115511e-01,
8.36517555e-01, 8.48367905e-01, 8.59911335e-01,
8.65610142e-01],
[9.84303877e-01, 9.74082937e-01, 9.58372986e-01,
9.46927810e-01, 9.34554991e-01, 9.10758513e-01,
8.91322841e-01, 8.68804460e-01, 8.43147543e-01,
8.29702355e-01, 7.85418525e-01, 7.55840616e-01,
7.00517755e-01, 6.14315731e-01, 5.74088249e-01,
5.56174903e-01, 5.33353699e-01, 5.27297943e-01,
5.16800783e-01, 5.08777243e-01, 5.04479841e-01,
5.00872221e-01, 4.99943609e-01, 4.99408317e-01,
4.99111122e-01, 4.99125255e-01, 4.99180374e-01,
4.99932942e-01],
[6.02712296e-17, 5.96465079e-17, 5.86911789e-17,
5.79996164e-17, 5.72570664e-17, 5.58501220e-17,
5.47267635e-17, 5.34739746e-17, 5.21120017e-17,
5.14308206e-17, 4.93839986e-17, 4.82263481e-17,
4.66770017e-17, 4.66770017e-17, 4.82263481e-17,
4.93839986e-17, 5.14308206e-17, 5.21120017e-17,
5.34739746e-17, 5.47267635e-17, 5.58501220e-17,
5.72570664e-17, 5.79996164e-17, 5.86911789e-17,
5.96465079e-17, 6.02712296e-17, 6.08831943e-17,
6.12082668e-17]]
np.testing.assert_allclose(path[:, ::10], path_steps_expected,
rtol=1e-5)
# now do the same for RTP coordinate system and also use a different
# model
# query for four phases
greatcircles = get_ray_paths(
inventory, catalog, phase_list=['P', 'PP', 'S', 'SS'],
coordinate_system='RTP', taup_model='ak135')
# two stations, three events, 4 phases should yield 24 rays (since all
# arrivals are encountered in this case)
assert len(greatcircles) == 24
# now check details of first ray
circ = greatcircles[0]
path = circ[0]
assert circ[1] == 'P'
assert circ[2] == 'NET.STA'
np.testing.assert_allclose(circ[3], otime.timestamp, atol=1e-5, rtol=0)
assert circ[4] == 7.0
assert circ[5] == 'smi:local/just-a-test'
assert circ[6] == 'smi:local/just-a-test2'
assert path.shape == (3, 270)
# now check some coordinates of the calculated path, start, end and
# some values in between
path_start_expected = [
[0.984304, 0.984217, 0.981165, 0.980880, 0.980595],
[1.570796, 1.570796, 1.570796, 1.570796, 1.570796],
[1.570796, 1.570745, 1.568935, 1.568765, 1.568595]]
np.testing.assert_allclose(path[:, :5], path_start_expected, rtol=1e-6)
path_end_expected = [
[0.998124, 0.999062, 0.999531, 0.999765, 1.000000],
[1.570796, 1.570796, 1.570796, 1.570796, 1.570796],
[0.524316, 0.523957, 0.523778, 0.523688, 0.523599]]
np.testing.assert_allclose(path[:, -5:], path_end_expected, rtol=1e-6)
path_steps_expected = [
[0.98430388, 0.97410140, 0.95847208, 0.94717382, 0.93508421,
0.91210171, 0.89145501, 0.86719412, 0.84963114, 0.83022016,
0.80548417, 0.78780343, 0.76158226, 0.76488692, 0.79524407,
0.80633663, 0.83604439, 0.85093573, 0.87434336, 0.89536778,
0.91994977, 0.93757572, 0.94908037, 0.95919008, 0.97447903,
0.98726847, 0.99568357],
[1.57079633, 1.57079633, 1.57079633, 1.57079633, 1.57079633,
1.57079633, 1.57079633, 1.57079633, 1.57079633, 1.57079633,
1.57079633, 1.57079633, 1.57079633, 1.57079633, 1.57079633,
1.57079633, 1.57079633, 1.57079633, 1.57079633, 1.57079633,
1.57079633, 1.57079633, 1.57079633, 1.57079633, 1.57079633,
1.57079633, 1.57079633],
[1.57079633, 1.56464945, 1.55454336, 1.54659206, 1.53738096,
1.51661510, 1.49423077, 1.46055656, 1.43236890, 1.39637248,
1.33996718, 1.28795961, 1.16342477, 0.91617848, 0.79110880,
0.76040906, 0.69478715, 0.66799188, 0.63152179, 0.60344720,
0.57851384, 0.56322297, 0.55460834, 0.54755404, 0.53770068,
0.53004245, 0.52531799]]
np.testing.assert_allclose(path[:, ::10], path_steps_expected,
rtol=1e-6)
def test_deep_source(self):
# Regression test -- check if deep sources are ok
model = TauPyModel("ak135")
arrivals = model.get_ray_paths(2000.0, 60.0, ["P"])
assert abs(arrivals[0].time - 480.32) < 1e-2