/
test_ray_paths.py
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/
test_ray_paths.py
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# -*- 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
"""
@classmethod
def setup_class(cls):
# 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
cls.path = os.path.join(os.path.dirname(__file__), 'images')
@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