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)
self.assertEqual(len(greatcircles), 24)
# now check details of first ray
circ = greatcircles[0]
path = circ[0]
self.assertEqual(circ[1], 'P')
self.assertEqual(circ[2], 'NET.STA')
np.testing.assert_allclose(circ[3], otime.timestamp, atol=1e-5, rtol=0)
self.assertEqual(circ[4], 7.0)
self.assertEqual(circ[5], 'smi:local/just-a-test')
self.assertEqual(circ[6], 'smi:local/just-a-test2')
self.assertEqual(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)
self.assertEqual(len(greatcircles), 24)
# now check details of first ray
circ = greatcircles[0]
path = circ[0]
self.assertEqual(circ[1], 'P')
self.assertEqual(circ[2], 'NET.STA')
np.testing.assert_allclose(circ[3], otime.timestamp, atol=1e-5, rtol=0)
self.assertEqual(circ[4], 7.0)
self.assertEqual(circ[5], 'smi:local/just-a-test')
self.assertEqual(circ[6], 'smi:local/just-a-test2')
self.assertEqual(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 _write_vtk_files(inventory,
catalog,
phase_list=('P'),
taup_model='iasp91'):
"""
internal vtk output routine. Check out the plot_rays routine
for more information on the parameters
"""
# define file names
fname_paths = 'paths.vtk'
fname_events = 'events.vtk'
fname_stations = 'stations.vtk'
# get 3d paths for all station/event combinations
greatcircles = get_ray_paths(inventory,
catalog,
phase_list=phase_list,
coordinate_system='XYZ',
taup_model=taup_model)
# sphere radii
r_earth_m = taup_model.model.radius_of_planet
r_earth = 1.0
r_cmb = (r_earth_m - taup_model.model.cmb_depth) / r_earth_m
r_iocb = (r_earth_m - taup_model.model.iocb_depth) / r_earth_m
# now assemble all points, stations and connectivity
stations = [] # unique list of stations
events = [] # unique list of events
lines = [] # contains the points that constitute each ray
npoints_tot = 0 # this is the total number of points of all rays
istart_ray = 0 # this is the first point of each ray in the loop
points = []
for gcircle, name, stlabel, time, magnitude, evid, _ in greatcircles:
date = UTCDateTime(time)
evlabel = '{:s} | M{:.1f}'.format(str(date.date), magnitude)
points_ray = gcircle[:, ::3] # use every third point
ndim, npoints_ray = points_ray.shape
iend_ray = istart_ray + npoints_ray
connect_ray = np.arange(istart_ray, iend_ray, dtype=int)
lines.append(connect_ray)
points.append(points_ray)
if stlabel not in stations:
stations.append((gcircle[:, -1], stlabel))
if evlabel not in events:
events.append((gcircle[:, 0], evlabel))
npoints_tot += npoints_ray
istart_ray += npoints_ray
# write the ICB, CMB and Earth surface in one file
with open(fname_stations, 'w') as vtk_file:
# write some header information
vtk_header = ('# vtk DataFile Version 2.0\n'
'station locations\n'
'ASCII\n'
'DATASET UNSTRUCTURED_GRID\n'
'POINTS {:d} float\n'.format(3))
vtk_file.write(vtk_header)
# write a long list of all points
for location, stlabel in []:
vtk_file.write('{:.4e} {:.4e} {:.4e}\n'.format(*location))
# write the ray paths in one file
with open(fname_paths, 'w') as vtk_file:
# write some header information
vtk_header = ('# vtk DataFile Version 2.0\n'
'3d ray paths\n'
'ASCII\n'
'DATASET UNSTRUCTURED_GRID\n'
'POINTS {:d} float\n'.format(npoints_tot))
vtk_file.write(vtk_header)
# write a long list of all points
for x, y, z in np.hstack(points).T:
vtk_file.write('{:.4e} {:.4e} {:.4e}\n'.format(x, y, z))
# now write connectivity
nlines = len(lines)
npoints_connect = npoints_tot + nlines
vtk_file.write('CELLS {:d} {:d}\n'.format(nlines, npoints_connect))
for line in lines:
vtk_file.write('{:d} '.format(len(line)))
for ipoint in line:
if ipoint % 30 == 29:
vtk_file.write('\n')
vtk_file.write('{:d} '.format(ipoint))
# cell types. 4 means cell type is a poly_line
vtk_file.write('\nCELL_TYPES {:d}\n'.format(nlines))
for line in lines:
vtk_file.write('4\n')
# write the stations in another file
with open(fname_stations, 'w') as vtk_file:
# write some header information
vtk_header = ('# vtk DataFile Version 2.0\n'
'station locations\n'
'ASCII\n'
'DATASET UNSTRUCTURED_GRID\n'
'POINTS {:d} float\n'.format(len(stations)))
vtk_file.write(vtk_header)
# write a long list of all points
for location, stlabel in stations:
vtk_file.write('{:.4e} {:.4e} {:.4e}\n'.format(*location))
# write the events in another file
with open(fname_events, 'w') as vtk_file:
# write some header information
vtk_header = ('# vtk DataFile Version 2.0\n'
'event locations\n'
'ASCII\n'
'DATASET UNSTRUCTURED_GRID\n'
'POINTS {:d} float\n'.format(len(events)))
vtk_file.write(vtk_header)
# write a long list of all points
for location, evlabel in events:
vtk_file.write('{:.4e} {:.4e} {:.4e}\n'.format(*location))
def _plot_rays_mayavi(inventory,
catalog,
phase_list=['P'],
colorscheme='default',
animate=False,
savemovie=False,
figsize=(800, 800),
taup_model='iasp91',
coastlines='internal',
icol=0,
event_labels=True,
station_labels=True,
fname_out=None,
view_dict=None):
"""
internal mayavi plotting routine. Check out the plot_rays routine
for more information on the parameters
"""
try:
from mayavi import mlab
except Exception as err:
print(err)
msg = ("obspy failed to import mayavi. "
"You need to install the mayavi module "
"(e.g. conda install mayavi, pip install mayavi). "
"If it is installed and still doesn't work, "
"try setting the environmental variable QT_API to "
"pyqt (e.g. export QT_API=pyqt) before running the "
"code. Another option is to avoid mayavi and "
"directly use kind='vtk' for vtk file output of the "
"radiation pattern that can be used by external "
"software like paraview")
raise ImportError(msg)
if isinstance(taup_model, str):
from obspy.taup import TauPyModel
model = TauPyModel(model=taup_model)
else:
model = taup_model
if fname_out is not None:
offscreen = True
else:
offscreen = False
nphases = len(phase_list)
greatcircles = get_ray_paths(inventory=inventory,
catalog=catalog,
phase_list=phase_list,
coordinate_system='XYZ',
taup_model=model)
if len(greatcircles) == 0:
raise ValueError('no paths found for the input stations and events')
# define colorschemes
if colorscheme == 'dark' or colorscheme == 'default':
# we use the color set that is used in taup, but adjust the lightness
# to get nice shiny rays that are well visible in the dark 3d plots
from obspy.taup.tau import COLORS
ncolors = len(COLORS)
color_hues = [rgb_to_hls(*hex2color(col))[0] for col in COLORS]
# swap green and red to start with red:
color_hues[2], color_hues[1] = color_hues[1], color_hues[2]
# first color is for the continents etc:
continent_hue = color_hues[0]
event_hue = color_hues[-1]
# the remaining colors are for the rays:
ray_hues = color_hues[1:-1]
# now convert all of the hues to rgb colors:
ray_light = 0.45
ray_sat = 1.0
raycolors = [
hls_to_rgb(ray_hues[(iphase + icol) % (ncolors - 2)], ray_light,
ray_sat) for iphase in range(nphases)
]
stationcolor = hls_to_rgb(continent_hue, 0.7, 0.7)
continentcolor = hls_to_rgb(continent_hue, 0.3, 0.2)
eventcolor = hls_to_rgb(event_hue, 0.7, 0.7)
cmbcolor = continentcolor
bgcolor = (0, 0, 0)
# sizes:
sttextsize = (0.015, 0.015, 0.015)
stmarkersize = 0.01
evtextsize = (0.015, 0.015, 0.015)
evmarkersize = 0.05
elif colorscheme == 'bright':
# colors (a distinct colors for each phase):
lightness = 0.2
saturation = 1.0
ncolors = nphases + 2 # two extra colors for continents and events
hues = np.linspace(0., 1. - 1. / ncolors, ncolors)
raycolors = [
hls_to_rgb(hue, lightness, saturation) for hue in hues[2:]
]
stationcolor = hls_to_rgb(hues[0], 0.2, 0.5)
continentcolor = hls_to_rgb(hues[0], 0.6, 0.2)
eventcolor = hls_to_rgb(hues[1], 0.2, 0.5)
cmbcolor = continentcolor
bgcolor = (1, 1, 1)
# sizes:
# everything has to be larger in bright background plot because it
# is more difficult to read
sttextsize = (0.02, 0.02, 0.02)
stmarkersize = 0.02
evtextsize = (0.02, 0.02, 0.02)
evmarkersize = 0.06
else:
raise ValueError('colorscheme {:s} not recognized'.format(colorscheme))
# assemble each phase and all stations/events to plot them in a single call
stations_loc = []
stations_lab = []
events_loc = []
events_lab = []
phases = [[] for iphase in range(nphases)]
for gcircle, name, stlabel, time, magnitude, evid, _ in greatcircles:
iphase = phase_list.index(name)
phases[iphase].append(gcircle)
date = UTCDateTime(time)
evlabel = '{:s} | M{:.1f}'.format(str(date.date), magnitude)
if stlabel not in stations_lab:
x, y, z = gcircle[0, -1], gcircle[1, -1], gcircle[2, -1]
stations_loc.append((x, y, z))
stations_lab.append(stlabel)
if evlabel not in events_lab:
x, y, z = gcircle[0, 0], gcircle[1, 0], gcircle[2, 0]
events_loc.append((x, y, z))
events_lab.append(evlabel)
# now begin mayavi plotting
if offscreen:
mlab.options.offscreen = True
fig = mlab.figure(size=figsize, bgcolor=bgcolor)
# define connectivity of each ray and add it to the scene
for iphase, phase in enumerate(phases):
index = 0
connects = []
for ray in phase:
ndim, npoints = ray.shape
connects.append(
np.vstack([
np.arange(index, index + npoints - 1.5),
np.arange(index + 1, index + npoints - .5)
]).T)
index += npoints
# collapse all points of the phase into a long array
if len(phase) > 1:
points = np.hstack(phase)
elif len(phase) == 1:
points = phase[0]
else:
continue
connects = np.vstack(connects)
# Create the points
src = mlab.pipeline.scalar_scatter(*points)
# Connect them
src.mlab_source.dataset.lines = connects
# The stripper filter cleans up connected lines
lines = mlab.pipeline.stripper(src)
color = raycolors[iphase]
mlab.pipeline.surface(lines, line_width=0.5, color=color)
# plot all stations
fig.scene.disable_render = True # Super duper trick
stxs, stys, stzs = zip(*stations_loc)
mlab.points3d(stxs,
stys,
stzs,
scale_factor=stmarkersize,
color=stationcolor)
if station_labels:
for loc, stlabel in zip(stations_loc, stations_lab):
mlab.text3d(loc[0],
loc[1],
loc[2],
stlabel,
scale=sttextsize,
color=stationcolor)
# plot all events
evxs, evys, evzs = zip(*events_loc)
evsource = mlab.pipeline.vector_scatter(evxs, evys, evzs, -np.array(evxs),
-np.array(evys), -np.array(evzs))
evmarkers = mlab.pipeline.glyph(evsource,
scale_factor=evmarkersize,
scale_mode='none',
color=eventcolor,
mode='cone',
resolution=8)
evmarkers.glyph.glyph_source.glyph_position = 'head'
if event_labels:
for loc, evlabel in zip(events_loc, events_lab):
mlab.text3d(loc[0],
loc[1],
loc[2],
evlabel,
scale=evtextsize,
color=eventcolor)
fig.scene.disable_render = False # Super duper trick
# read and plot coastlines
if coastlines == 'internal':
from mayavi.sources.builtin_surface import BuiltinSurface
data_source = BuiltinSurface(source='earth', name="Continents")
data_source.data_source.on_ratio = 1
else:
data_source = mlab.pipeline.open(coastlines)
coastmesh = mlab.pipeline.surface(data_source,
opacity=1.0,
line_width=0.5,
color=continentcolor)
coastmesh.actor.actor.scale = np.array([1.02, 1.02, 1.02])
# plot block sphere that hides the backside of the continents
rad = 0.99
phi, theta = np.mgrid[0:np.pi:51j, 0:2 * np.pi:51j]
x = rad * np.sin(phi) * np.cos(theta)
y = rad * np.sin(phi) * np.sin(theta)
z = rad * np.cos(phi)
blocksphere = mlab.mesh(x, y, z, color=bgcolor)
blocksphere.actor.property.frontface_culling = True # front not rendered
# make CMB sphere
r_earth = model.model.radius_of_planet
r_cmb = r_earth - model.model.cmb_depth
rad = r_cmb / r_earth
phi, theta = np.mgrid[0:np.pi:201j, 0:2 * np.pi:201j]
x = rad * np.sin(phi) * np.cos(theta)
y = rad * np.sin(phi) * np.sin(theta)
z = rad * np.cos(phi)
cmb = mlab.mesh(x, y, z, color=cmbcolor, opacity=0.3, line_width=0.5)
cmb.actor.property.interpolation = 'gouraud'
# cmb.actor.property.interpolation = 'flat'
# make ICB sphere
r_iocb = r_earth - model.model.iocb_depth
rad = r_iocb / r_earth
phi, theta = np.mgrid[0:np.pi:101j, 0:2 * np.pi:101j]
x = rad * np.sin(phi) * np.cos(theta)
y = rad * np.sin(phi) * np.sin(theta)
z = rad * np.cos(phi)
icb = mlab.mesh(x, y, z, color=cmbcolor, opacity=0.3, line_width=0.5)
icb.actor.property.interpolation = 'gouraud'
if view_dict is None:
view_dict = {
'azimuth': 0.,
'elevation': 90.,
'distance': 4.,
'focalpoint': (0., 0., 0.)
}
mlab.view(**view_dict)
# to make a movie from the image files, you can use the command:
# avconv -qscale 5 -r 20 -b 9600 -i %05d.png -vf scale=800:752 movie.mp4
if animate and not offscreen:
@mlab.show
@mlab.animate(delay=20)
def anim():
iframe = 0
while 1:
if savemovie and iframe < 360:
mlab.savefig('{:05d}.png'.format(iframe))
# camera moves from East to West opposite of Earth's rotation
fig.scene.camera.azimuth(-1.)
fig.scene.render()
iframe += 1
yield
anim() # Starts the animation.
else:
if offscreen:
mlab.savefig(fname_out)
else:
mlab.show()
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)
self.assertEqual(len(greatcircles), 24)
# now check details of first ray
circ = greatcircles[0]
path = circ[0]
self.assertEqual(circ[1], 'P')
self.assertEqual(circ[2], 'NET.STA')
np.testing.assert_allclose(circ[3], otime.timestamp, atol=1e-5, rtol=0)
self.assertEqual(circ[4], 7.0)
self.assertEqual(circ[5], 'smi:local/just-a-test')
self.assertEqual(circ[6], 'smi:local/just-a-test2')
self.assertEqual(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)
self.assertEqual(len(greatcircles), 24)
# now check details of first ray
circ = greatcircles[0]
path = circ[0]
self.assertEqual(circ[1], 'P')
self.assertEqual(circ[2], 'NET.STA')
np.testing.assert_allclose(circ[3], otime.timestamp, atol=1e-5, rtol=0)
self.assertEqual(circ[4], 7.0)
self.assertEqual(circ[5], 'smi:local/just-a-test')
self.assertEqual(circ[6], 'smi:local/just-a-test2')
self.assertEqual(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)