def ArrayBeamForming(sx, sy, traces, t_i_c, t_e_c, nsta , center, lat, lon, t_o, deltat):
'''
'''
PP = np.zeros([sx.size *sy.size])
P = np.zeros([sx.size, sy.size])
dis_x = orthodrome.distance_accurate50m_numpy( lat[center], lon, lat[center], lon[center])
dis_y = orthodrome.distance_accurate50m_numpy( lat, lon[center], lat[center], lon[center])
print "dis_x[center]",dis_x[center]
dis_x[center]=0.0
dis_y[center]=0.0
dum_ = np.where( lat > lat[center])
dis_y[dum_] = - 1.0* dis_y[dum_]
dum = np.where(lon > lon[center])
dis_x[dum] = - 1.0*dis_x[dum]
X = np.array([dis_x, dis_y])
S = np.transpose([np.tile(sx, np.size(sy)), np.repeat(sy, np.size(sx))])
delay = np.dot(S,X)
# t0 = time.time()
PP = abeam(traces, delay, t_i_c, t_e_c)
# t1 = time.time()
# Q = Abeam(traces, delay, t_i_c, t_e_c)
# PP = Q.trace_beam()
# print PP.dtype
# t2 = time.time()
# print t1 - t0
P = PP.reshape(sx.size, sy.size)
P = P.transpose()
return P, delay
def array_beam_s(center, lat, lon, Sx, Sy, traces, dum_tr):
nsta = np.size(traces)
delay = np.zeros(nsta)
traces_d = []
for st in range(0,nsta,1):
if st != center :
dis_x = orthodrome.distance_accurate50m_numpy( lat[center], lon[st], lat[center], lon[center])
dis_y = orthodrome.distance_accurate50m_numpy( lat[st], lon[center], lat[center], lon[center])
if lat[st] > lat[center]:
dis_y = -1.0*dis_y
if lon[st] > lon[center]:
dis_x = -1.0*dis_x
delay[st] = dis_x * Sx + dis_y * Sy
else:
delay[st] = 0.0
dd = traces[st].copy()
dd.shift(delay[st])
dum_tr.add(dd)
traces_d.append(dd)
traces_d.append(dum_tr)
return traces_d, delay
def testDistanceArrayPythonC(self):
ntest = 10000
locs = self.get_critical_random_locations(ntest)
a = orthodrome.distance_accurate50m_numpy(*locs,
implementation='python')
b = orthodrome.distance_accurate50m_numpy(*locs, implementation='c')
num.testing.assert_array_almost_equal(a, b)
def testDistanceArrayPythonC(self):
ntest = 10000
locs = self.get_critical_random_locations(ntest)
a = orthodrome.distance_accurate50m_numpy(
*locs,
implementation='python')
b = orthodrome.distance_accurate50m_numpy(
*locs,
implementation='c')
num.testing.assert_array_almost_equal(a, b)
def surface_distance(alat, alon, anorth, aeast, blat, blon, bnorth, beast):
args = float_array_broadcast(alat, alon, anorth, aeast, blat, blon, bnorth,
beast)
want_scalar = False
if len(args[0].shape) == 0:
want_scalar = True
args = [num.atleast_1d(x) for x in args]
(alat, alon, anorth, aeast, blat, blon, bnorth, beast) = args
eqref = num.logical_and(alat == blat, alon == blon)
neqref = num.logical_not(eqref)
dist = num.empty_like(alat)
dist[eqref] = num.sqrt((bnorth[eqref] - anorth[eqref])**2 +
(beast[eqref] - aeast[eqref])**2)
aalat, aalon = od.ne_to_latlon(alat[neqref], alon[neqref], anorth[neqref],
aeast[neqref])
bblat, bblon = od.ne_to_latlon(blat[neqref], blon[neqref], bnorth[neqref],
beast[neqref])
dist[neqref] = od.distance_accurate50m_numpy(aalat, aalon, bblat, bblon)
if want_scalar:
return dist[0]
else:
return dist
def test_midpoint(self):
center_lons = num.linspace(0., 180., 5)
center_lats = [0., 89.]
npoints = 10000
half_side_length = 1000000.
distance_error_max = 50000.
for lat in center_lats:
for lon in center_lons:
n = num.random.uniform(
-half_side_length, half_side_length, npoints)
e = num.random.uniform(
-half_side_length, half_side_length, npoints)
dlats, dlons = orthodrome.ne_to_latlon(lat, lon, n, e)
clat, clon = orthodrome.geographic_midpoint(dlats, dlons)
d = orthodrome.distance_accurate50m_numpy(
clat, clon, lat, lon)[0]
if plot:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(n, e)
c_n, c_e = orthodrome.latlon_to_ne_numpy(
lat, lon, clat, clon)
ax.plot(c_n, c_e, 'ro')
plt.show()
self.assertTrue(d < distance_error_max, 'Distance %s > %s' %
(d, distance_error_max) +
'(maximum error)\n tested lat/lon: %s/%s' %
(lat, lon))
def get_distance_hypo(eventi, eventj):
'''
Normalized Euclidean hypocentral distance, assuming flat earth to combine
epicentral distance and depth difference.
The normalization assumes largest considered distance is 1000 km.
'''
maxdist_km = 1000.
a_lats, a_lons, b_lats, b_lons = \
eventi.north, eventi.east, eventj.north, eventj.east
a_dep, b_dep = eventi.down, eventj.down
if (a_lats == b_lats) and (a_lons == b_lons) and (a_dep == b_dep):
d = 0.
else:
distance_m = orthodrome.distance_accurate50m_numpy(
a_lats, a_lons, b_lats, b_lons)
distance_km = distance_m / 1000.
ddepth = abs(eventi.down - eventj.down)
hypo_distance_km = math.sqrt(
distance_km * distance_km + ddepth * ddepth)
# maxdist = float(inv_param['EUCLIDEAN_MAX'])
d = hypo_distance_km / maxdist_km
if d >= 1.:
d = 1.
return d
def window(lat, lon, lat_source, lon_source, depth_source, nsta, store):
center = Array_center(lat, lon)
source_reciever_dis = orthodrome.distance_accurate50m_numpy(
lat_source, lon_source, lat, lon)
t_p = np.array([
store.t("first(p|P)", (depth_source, int(source_reciever_dis[i])))
for i in range(0, nsta)])
t_s = np.array([
store.t("first(s|S)", (depth_source, int(source_reciever_dis[i])))
for i in range(0, nsta)])
t_origin = util.str_to_time('2008-02-17 11:06:01.10')
def win_(t_, t_l , t_r, center):
wind_i = t_origin + t_ - t_l
wind_e = t_origin + t_ + t_r
t_o = - t_ + t_[center]
return wind_i, wind_e, t_o
P_wind_i, P_wind_e, t_op = win_(t_p, 5.0 , 20.0, center)
S_wind_i, S_wind_e, t_os = win_(t_s, 2.0 , 18.0, center)
return P_wind_i, P_wind_e, t_op , center#, S_wind_i, S_wind_e, t_os
def test_midpoint(self):
center_lons = num.linspace(0., 180., 5)
center_lats = [0., 89.]
npoints = 10000
half_side_length = 1000000.
distance_error_max = 50000.
for lat in center_lats:
for lon in center_lons:
n = num.random.uniform(-half_side_length, half_side_length,
npoints)
e = num.random.uniform(-half_side_length, half_side_length,
npoints)
dlats, dlons = orthodrome.ne_to_latlon(lat, lon, n, e)
clat, clon = orthodrome.geographic_midpoint(dlats, dlons)
d = orthodrome.distance_accurate50m_numpy(
clat, clon, lat, lon)[0]
if plot:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(n, e)
c_n, c_e = orthodrome.latlon_to_ne_numpy(
lat, lon, clat, clon)
ax.plot(c_n, c_e, 'ro')
plt.show()
self.assertTrue(
d < distance_error_max,
'Distance %s > %s' % (d, distance_error_max) +
'(maximum error)\n tested lat/lon: %s/%s' % (lat, lon))
def distance_to(self, source):
src_lats = num_full_like(self.lats, fill_value=source.lat)
src_lons = num_full_like(self.lons, fill_value=source.lon)
target_coords = self.get_latlon()
target_lats = target_coords[:, 0]
target_lons = target_coords[:, 1]
return distance_accurate50m_numpy(
src_lats, src_lons, target_lats, target_lons)
def distance_to(self, source):
src_lats = num_full_like(self.lats, fill_value=source.lat)
src_lons = num_full_like(self.lons, fill_value=source.lon)
target_coords = self.get_latlon()
target_lats = target_coords[:, 0]
target_lons = target_coords[:, 1]
return distance_accurate50m_numpy(
src_lats, src_lons, target_lats, target_lons)
def distances_to(self, receiver):
if self.same_origin(receiver):
return num.sqrt((self.north_shifts - receiver.north_shift)**2 +
(self.east_shifts - receiver.east_shift)**2)
else:
slats, slons = self.effective_latlons
rlat, rlon = receiver.effective_latlon
return orthodrome.distance_accurate50m_numpy(slats, slons,
rlat, rlon)
def get_radius(self):
stations = self.get_stations()
if not stations:
return self._parent.get_radius()
clat, clon = self.get_center_latlon()
radii = distance_accurate50m_numpy(
clat, clon, [st.effective_lat for st in stations],
[st.effective_lon for st in stations])
return float(radii.max())
def get_cities(lat, lon, radius, minpop=0):
region = od.radius_to_region(lat, lon, radius)
cities = get_cities_region(region, minpop=minpop)
clats = num.array([c.lat for c in cities])
clons = num.array([c.lon for c in cities])
dists = od.distance_accurate50m_numpy(lat, lon, clats, clons)
order = num.argsort(dists)
cities_sorted = [cities[i] for i in order if dists[i] < radius]
return cities_sorted
def get_cities(lat, lon, radius, minpop=0):
region = od.radius_to_region(lat, lon, radius)
cities = get_cities_region(region, minpop=minpop)
clats = num.array([c.lat for c in cities])
clons = num.array([c.lon for c in cities])
dists = od.distance_accurate50m_numpy(lat, lon, clats, clons)
order = num.argsort(dists)
cities_sorted = [cities[i] for i in order if dists[i] < radius]
return cities_sorted
def getDistance2Event(self, event):
'''
:param event: instanceof pyrocko.model.Event
:returns: [float] Distance between given event and self
'''
assert isinstance(event, model.Event), 'event not instance of model.Event'
try:
return orthodrome.distance_accurate50m_numpy( event.lat,
event.lon,self.latitude,self.longitude)
except TypeError:
sys.stdout.write('TypeError. Most likely no latitude,\n\
longitude for station:'), self.network, self.station
def distance_to(self, other):
'''
Compute distance [m] to other location object.
'''
if self.same_origin(other):
return math.sqrt((self.north_shift - other.north_shift)**2 +
(self.east_shift - other.east_shift)**2)
else:
slat, slon = self.effective_latlon
rlat, rlon = other.effective_latlon
return orthodrome.distance_accurate50m_numpy(slat, slon,
rlat, rlon)
def test_point_in_polygon(self):
if plot:
import matplotlib.pyplot as plt
for i in range(100):
if plot:
plt.clf()
axes = plt.gca()
lat0, lon0, radius, circle = random_circle(100)
if plot:
print(lat0, lon0, radius)
lats = num.linspace(-90., 90., 100)
lons = num.linspace(-180., 180., 200)
points = num.empty((lons.size * lats.size, 2))
points[:, 0] = num.repeat(lats, lons.size)
points[:, 1] = num.tile(lons, lats.size)
mask = orthodrome.contains_points(circle, points)
distances = orthodrome.distance_accurate50m_numpy(
lat0, lon0, points[:, 0], points[:, 1])
mask2 = distances < radius
mask3 = num.logical_and(
num.not_equal(mask2, mask),
num.abs(distances - radius) > radius / 100.)
if plot:
axes.plot(circle[:, 1], circle[:, 0], 'o', ms=1, color='black')
axes.plot(points[mask, 1],
points[mask, 0],
'o',
ms=1,
alpha=0.2,
color='black')
axes.plot(points[mask3, 1],
points[mask3, 0],
'o',
ms=1,
color='red')
if plot:
plt.show()
assert not num.any(mask3)
def grid_points_in_error_ellipses(lat_ev, lon_ev, depth_ev, error_h, error_z,
path_models=None, gf_store_id=None):
pathlist = Path(path_models).glob('model_%s_*' % gf_store_id)
region = orthodrome.radius_to_region(lat_ev, lon_ev, error_h)
grid_points = []
for path in sorted(pathlist):
path = str(path)
model_coordinates = path.split("_")
lat = float(model_coordinates[3])
lon = float(model_coordinates[4])
depth = float(model_coordinates[5])
dists = orthodrome.distance_accurate50m_numpy(lat_ev, lon_ev, lat, lon)
if dists < error_h:
if depth_ev-error_z < depth and depth_ev+error_z > depth:
grid_points.append(path)
return grid_points
def random_conf(Ns):
lats=np.random.uniform(20,40,Ns)
lons=np.random.uniform(20,40,Ns)
lats = np.around(lats, decimals=1)
lons = np.around(lons, decimals=1)
dis=np.zeros([Ns,Ns])
for i in range(0,Ns):
dis[i] = orthodrome.distance_accurate50m_numpy(lats, lons, lats[i], lons[i])
dis /= 1000.0
dis = np.around(dis, decimals=0)
iu1 = np.triu_indices(Ns)
dis[iu1]=0
ar_mindis = dis[np.nonzero(dis)].min()
ar_aperture = dis[np.nonzero(dis)].max()
return lats, lons, ar_mindis, ar_aperture
def distance_to(self, other):
'''
Compute surface distance [m] to other location object.
'''
if self.same_origin(other):
other_north_shift, other_east_shift = get_offset(other)
return math.sqrt((self.north_shift - other_north_shift)**2 +
(self.east_shift - other_east_shift)**2)
else:
slat, slon = self.effective_latlon
rlat, rlon = get_effective_latlon(other)
return float(orthodrome.distance_accurate50m_numpy(
slat, slon, rlat, rlon)[0])
def subset_events_dist_cat(catalog, mag_min, mag_max,
tmin, tmax, st_lat, st_lon,
dist_min=None, dist_max=None):
"""
Extract a subset of events from event catalog
:param catalog: Event catalog in pyrocko format
:param mag_min: Min. magnitude
:param mag_max: Max. magnitude
:param tmin: string representing UTC time
:param tmax: string representing UTC time
:param format tmin: time string format ('%Y-%m-%d %H:%M:%S.OPTFRAC')
:param format tmax: time string format ('%Y-%m-%d %H:%M:%S.OPTFRAC')
:param dist_min: min. distance (km)
:param dist_max: max. distance (km)
:param depth_min
:param depth_max
:returns: list of events
"""
use_events = []
events = model.load_events(catalog)
for ev in events:
if ev.magnitude < mag_max and\
ev.magnitude > mag_min and\
ev.time < util.str_to_time(tmax) and\
ev.time > util.str_to_time(tmin):
if dist_min or dist_max:
dist = orthodrome.distance_accurate50m_numpy(
ev.lat, ev.lon, st_lat, st_lon)/1000.
if dist_min and dist_max and\
dist > dist_min and dist < dist_max:
use_events.append(ev)
if dist_min and not dist_max and dist > dist_min:
use_events.append(ev)
if dist_max and not dist_min and dist < dist_max:
use_events.append(ev)
return(use_events)
def get_center_station(stations, select_closest=False):
''' gravitations center of *stations* list.'''
n = len(stations)
slats = num.empty(n)
slons = num.empty(n)
for i, s in enumerate(stations):
slats[i] = s.lat
slons[i] = s.lon
center_lat, center_lon = ortho.geographic_midpoint(slats, slons)
if select_closest:
center_lats = num.ones(n) * center_lat
center_lons = num.ones(n) * center_lon
dists = ortho.distance_accurate50m_numpy(center_lats, center_lons,
slats, slons)
return stations[num.argmin(dists)]
else:
return model.Station(center_lat, center_lon)
def get_center_station(stations, select_closest=False):
''' gravitations center of *stations* list.'''
n = len(stations)
slats = num.empty(n)
slons = num.empty(n)
for i, s in enumerate(stations):
slats[i] = s.lat
slons[i] = s.lon
center_lat, center_lon = ortho.geographic_midpoint(slats, slons)
if select_closest:
center_lats = num.ones(n)*center_lat
center_lons = num.ones(n)*center_lon
dists = ortho.distance_accurate50m_numpy(
center_lats, center_lons, slats, slons)
return stations[num.argmin(dists)]
else:
return model.Station(center_lat, center_lon)
def subset_events_dist_evlist(ev_list, mag_min, mag_max,
tmin, tmax, st_lat, st_lon,
depth_min, depth_max,
dist_min=None, dist_max=None):
"""
Extract a subset of events from event catalog
:param ev_list: list of pyrocko events
:param mag_min: Min. magnitude
:param mag_max: Max. magnitude
:param tmin: string representing UTC time
:param tmax: string representing UTC time
:param format tmin: time string format ('%Y-%m-%d %H:%M:%S.OPTFRAC')
:param format tmax: time string format ('%Y-%m-%d %H:%M:%S.OPTFRAC')
:param dist_min: min. distance (km)
:param dist_max: max. distance (km)
:param depth_min
:param depth_max
:returns: list of events
"""
use_events = []
for ev in ev_list:
if (mag_min < ev.magnitude < mag_max) and \
(util.str_to_time(tmin) < ev.time < util.str_to_time(tmax)) and \
(float(depth_min) < ev.depth < float(depth_max)):
if dist_min or dist_max:
dist = orthodrome.distance_accurate50m_numpy(
ev.lat, ev.lon, st_lat, st_lon)/1000.
if dist_min and dist_max and dist > dist_min and dist < dist_max:
use_events.append(ev)
if dist_min and not dist_max and dist > dist_min:
use_events.append(ev)
if dist_max and not dist_min and dist < dist_max:
use_events.append(ev)
return(use_events)
def update_distances(self, indices=None):
'''Calculate and update distances between events.'''
indices = indices or [[]]
indices = [i for ii in indices for i in ii]
if len(indices) != 1 or self.marker_table_view.horizontalHeader()\
.isSectionHidden(_column_mapping['Dist [km]']):
return
if self.last_active_event == self.pile_viewer.get_active_event():
return
else:
self.last_active_event = self.pile_viewer.get_active_event()
markers = self.pile_viewer.markers
omarker = markers[indices[0]]
if not isinstance(omarker, EventMarker):
return
emarkers = [m for m in markers if isinstance(m, EventMarker)]
if len(emarkers) < 2:
return
lats = num.zeros(len(emarkers))
lons = num.zeros(len(emarkers))
for i in xrange(len(emarkers)):
lats[i] = emarkers[i].get_event().lat
lons[i] = emarkers[i].get_event().lon
olats = num.zeros(len(emarkers))
olons = num.zeros(len(emarkers))
olats[:] = omarker.get_event().lat
olons[:] = omarker.get_event().lon
dists = orthodrome.distance_accurate50m_numpy(lats, lons, olats, olons)
dists /= 1000.
self.distances = dict(zip(emarkers, dists))
self.marker_table_view.viewport().repaint()
self.emit(qc.SIGNAL('dataChanged()'))
if self.marker_table_view.horizontalHeader().sortIndicatorSection() ==\
_column_mapping['Dist [km]']:
self.sort(_column_mapping['Dist [km]'])
def AperMin(lat, lon):
'''
This function returns array aperture and minimum distance between stations.
inputs are a set of lat and lon of stations.
'''
Ns = len(lat)
dis=np.zeros([Ns, Ns])
for i in range(0,Ns):
dis[i] = orthodrome.distance_accurate50m_numpy(lats, lons, lats[i], lons[i])
dis /= 1000.0
dis = np.around(dis, decimals=0)
iu1 = np.triu_indices(Ns)
dis[iu1]=0
ar_mindis = dis[np.nonzero(dis)].min()
ar_aperture = dis[np.nonzero(dis)].max()
return ar_mindis, ar_aperture
def update_distances(self, indices):
'''Calculate and update distances between events.'''
if len(indices) != 1 or self.marker_table_view.horizontalHeader()\
.isSectionHidden(_column_mapping['Dist [km]']):
return
if self.last_active_event == self.pile_viewer.get_active_event():
return
else:
self.last_active_event = self.pile_viewer.get_active_event()
index = indices[0]
markers = self.pile_viewer.markers
omarker = markers[index]
if not isinstance(omarker, EventMarker):
return
emarkers = [m for m in markers if isinstance(m, EventMarker)]
if len(emarkers) < 2:
return
lats = num.zeros(len(emarkers))
lons = num.zeros(len(emarkers))
for i in xrange(len(emarkers)):
lats[i] = emarkers[i].get_event().lat
lons[i] = emarkers[i].get_event().lon
olats = num.zeros(len(emarkers))
olons = num.zeros(len(emarkers))
olats[:] = omarker.get_event().lat
olons[:] = omarker.get_event().lon
dists = orthodrome.distance_accurate50m_numpy(lats, lons, olats, olons)
dists /= 1000.
self.distances = dict(zip(emarkers, dists))
self.emit(SIGNAL('dataChanged()'))
# expensive!
self.reset()
def get_distance_epi(eventi, eventj):
'''Normalized Euclidean epicentral distance.
The normalization assumes largest considered distance is 1000 km.
'''
maxdist_km = 1000.
a_lats, a_lons, b_lats, b_lons = \
eventi.lat, eventi.lon, eventj.lat, eventj.lon
if (a_lats == b_lats) and (a_lons == b_lons):
d = 0.
else:
distance_m = orthodrome.distance_accurate50m_numpy(
a_lats, a_lons, b_lats, b_lons)
distance_km = distance_m / 1000.
d = distance_km / maxdist_km
if d >= 1.:
d = 1.
return d
def distance_to(self, other):
'''
Compute surface distance [m] to other location object.
'''
if self.same_origin(other):
if isinstance(other, Location):
return math.sqrt((self.north_shift - other.north_shift)**2 +
(self.east_shift - other.east_shift)**2)
else:
return 0.0
else:
slat, slon = self.effective_latlon
try:
rlat, rlon = other.effective_latlon
except AttributeError:
rlat, rlon = other.lat, other.lon
return float(orthodrome.distance_accurate50m_numpy(
slat, slon, rlat, rlon)[0])
def distance_to(self, other):
'''
Compute surface distance [m] to other location object.
'''
if self.same_origin(other):
if isinstance(other, Location):
return math.sqrt((self.north_shift - other.north_shift)**2 +
(self.east_shift - other.east_shift)**2)
else:
return 0.0
else:
slat, slon = self.effective_latlon
try:
rlat, rlon = other.effective_latlon
except AttributeError:
rlat, rlon = other.lat, other.lon
return float(
orthodrome.distance_accurate50m_numpy(slat, slon, rlat,
rlon)[0])
def testDistanceArrayC(self):
ntest = 10000
locs = self.get_critical_random_locations(ntest)
orthodrome.distance_accurate50m_numpy(*locs, implementation='c')
def main(args=None):
if args is None:
args = sys.argv[1:]
parser = OptionParser(
usage=usage,
description=description)
parser.add_option(
'--width',
dest='width',
type='float',
default=20.0,
metavar='FLOAT',
help='set width of output image [cm] (%default)')
parser.add_option(
'--height',
dest='height',
type='float',
default=15.0,
metavar='FLOAT',
help='set height of output image [cm] (%default)')
parser.add_option(
'--topo-resolution-min',
dest='topo_resolution_min',
type='float',
default=40.0,
metavar='FLOAT',
help='minimum resolution of topography [dpi] (%default)')
parser.add_option(
'--topo-resolution-max',
dest='topo_resolution_max',
type='float',
default=200.0,
metavar='FLOAT',
help='maximum resolution of topography [dpi] (%default)')
parser.add_option(
'--no-grid',
dest='show_grid',
default=True,
action='store_false',
help='don\'t show grid lines')
parser.add_option(
'--no-topo',
dest='show_topo',
default=True,
action='store_false',
help='don\'t show topography')
parser.add_option(
'--no-cities',
dest='show_cities',
default=True,
action='store_false',
help='don\'t show cities')
parser.add_option(
'--no-illuminate',
dest='illuminate',
default=True,
action='store_false',
help='deactivate artificial illumination of topography')
parser.add_option(
'--illuminate-factor-land',
dest='illuminate_factor_land',
type='float',
metavar='FLOAT',
help='set factor for artificial illumination of land (0.5)')
parser.add_option(
'--illuminate-factor-ocean',
dest='illuminate_factor_ocean',
type='float',
metavar='FLOAT',
help='set factor for artificial illumination of ocean (0.25)')
parser.add_option(
'--theme',
choices=['topo', 'soft'],
default='topo',
help='select color theme, available: topo, soft (topo)"')
parser.add_option(
'--download-etopo1',
dest='download_etopo1',
action='store_true',
help='download full ETOPO1 topography dataset')
parser.add_option(
'--download-srtmgl3',
dest='download_srtmgl3',
action='store_true',
help='download full SRTMGL3 topography dataset')
parser.add_option(
'--make-decimated-topo',
dest='make_decimated',
action='store_true',
help='pre-make all decimated topography datasets')
parser.add_option(
'--stations',
dest='stations_fn',
metavar='FILENAME',
help='load station coordinates from FILENAME')
parser.add_option(
'--events',
dest='events_fn',
metavar='FILENAME',
help='load event coordinates from FILENAME')
parser.add_option(
'--debug',
dest='debug',
action='store_true',
default=False,
help='print debugging information to stderr')
(options, args) = parser.parse_args(args)
if options.debug:
util.setup_logging(program_name, 'debug')
else:
util.setup_logging(program_name, 'info')
if options.download_etopo1:
import pyrocko.datasets.topo.etopo1
pyrocko.datasets.topo.etopo1.download()
if options.download_srtmgl3:
import pyrocko.datasets.topo.srtmgl3
pyrocko.datasets.topo.srtmgl3.download()
if options.make_decimated:
import pyrocko.datasets.topo
pyrocko.datasets.topo.make_all_missing_decimated()
if (options.download_etopo1 or options.download_srtmgl3 or
options.make_decimated) and len(args) == 0:
sys.exit(0)
if options.theme == 'soft':
color_kwargs = {
'illuminate_factor_land': options.illuminate_factor_land or 0.2,
'illuminate_factor_ocean': options.illuminate_factor_ocean or 0.15,
'color_wet': (216, 242, 254),
'color_dry': (238, 236, 230),
'topo_cpt_wet': 'light_sea_uniform',
'topo_cpt_dry': 'light_land_uniform'}
elif options.theme == 'topo':
color_kwargs = {
'illuminate_factor_land': options.illuminate_factor_land or 0.5,
'illuminate_factor_ocean': options.illuminate_factor_ocean or 0.25}
events = []
if options.events_fn:
events = model.load_events(options.events_fn)
stations = []
if options.stations_fn:
stations = model.load_stations(options.stations_fn)
if not (len(args) == 4 or (
len(args) == 1 and (events or stations))):
parser.print_help()
sys.exit(1)
if len(args) == 4:
try:
lat = float(args[0])
lon = float(args[1])
radius = float(args[2])*km
except Exception:
parser.print_help()
sys.exit(1)
else:
lats, lons = latlon_arrays(stations+events)
lat, lon = map(float, od.geographic_midpoint(lats, lons))
radius = float(
num.max(od.distance_accurate50m_numpy(lat, lon, lats, lons)))
radius *= 1.1
m = automap.Map(
width=options.width,
height=options.height,
lat=lat,
lon=lon,
radius=radius,
topo_resolution_max=options.topo_resolution_max,
topo_resolution_min=options.topo_resolution_min,
show_topo=options.show_topo,
show_grid=options.show_grid,
illuminate=options.illuminate,
**color_kwargs)
logger.debug('map configuration:\n%s' % str(m))
if options.show_cities:
m.draw_cities()
if stations:
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
m.gmt.psxy(
in_columns=(lons, lats),
S='t8p',
G='black',
*m.jxyr)
for s in stations:
m.add_label(s.lat, s.lon, '%s' % '.'.join(
x for x in s.nsl() if x))
if events:
beachball_symbol = 'mt'
beachball_size = 20.0
for ev in events:
if ev.moment_tensor is None:
m.gmt.psxy(
in_rows=[[ev.lon, ev.lat]],
S='c12p',
G=gmtpy.color('scarletred2'),
W='1p,black',
*m.jxyr)
else:
devi = ev.moment_tensor.deviatoric()
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)
if m.gmt.is_gmt5():
kwargs = dict(
M=True,
S='%s%g' % (
beachball_symbol[0], beachball_size / gmtpy.cm))
else:
kwargs = dict(
S='%s%g' % (
beachball_symbol[0], beachball_size*2 / gmtpy.cm))
m.gmt.psmeca(
in_rows=[data],
G=gmtpy.color('chocolate1'),
E='white',
W='1p,%s' % gmtpy.color('chocolate3'),
*m.jxyr,
**kwargs)
m.save(args[-1])
def optparse(
required=(),
optional=(),
args=sys.argv,
usage='%prog [options]',
descr=None):
want = required + optional
parser = OptionParser(
prog='cake',
usage=usage,
description=descr.capitalize()+'.',
add_help_option=False,
formatter=util.BetterHelpFormatter())
parser.add_option(
'-h', '--help', action='help', help='Show help message and exit.')
if 'phases' in want:
group = OptionGroup(parser, 'Phases', '''
Seismic phase arrivals may be either specified as traditional phase names (e.g.
P, S, PP, PcP, ...) or in Cake's own syntax which is more powerful. Use the
--classic option, for traditional phase names. Use the --phase option if you
want to define phases in Cake's syntax.
''')
group.add_option(
'--phase', '--phases', dest='phases', action="append",
default=[], metavar='PHASE1,PHASE2,...',
help='''Comma separated list of seismic phases in Cake\'s syntax.
The definition of a seismic propagation path in Cake's phase syntax is a string
consisting of an alternating sequence of "legs" and "knees".
A "leg" represents seismic wave propagation without any conversions,
encountering only super-critical reflections. Legs are denoted by "P", "p",
"S", or "s". The capital letters are used when the take-off of the "leg" is
in downward direction, while the lower case letters indicate a take-off in
upward direction.
A "knee" is an interaction with an interface. It can be a mode conversion, a
reflection, or propagation as a headwave or diffracted wave.
* conversion is simply denoted as: "(INTERFACE)" or "DEPTH"
* upperside reflection: "v(INTERFACE)" or "vDEPTH"
* underside reflection: "^(INTERFACE)" or "^DEPTH"
* normal kind headwave or diffracted wave: "v_(INTERFACE)" or "v_DEPTH"
The interface may be given by name or by depth: INTERFACE is the name of an
interface defined in the model, DEPTH is the depth of an interface in [km] (the
interface closest to that depth is chosen). If two legs appear consecutively
without an explicit "knee", surface interaction is assumed.
The preferred standard interface names in cake are "conrad", "moho", "cmb"
(core-mantle boundary), and "cb" (inner core boundary).
The phase definition may end with a backslash "\\", to indicate that the ray
should arrive at the receiver from above instead of from below. It is possible
to restrict the maximum and minimum depth of a "leg" by appending
"<(INTERFACE)" or "<DEPTH" or ">(INTERFACE)" or ">DEPTH" after the leg
character, respectively.
When plotting rays or travel-time curves, the color can be set by appending
"{COLOR}" to the phase definition, where COLOR is the name of a color or an RGB
or RGBA color tuple in the format "R/G/B" or "R/G/B/A", respectively. The
values can be normalized to the range [0, 1] or to [0, 255]. The latter is only
assumed when any of the values given exceeds 1.0.
''')
group.add_option(
'--classic', dest='classic_phases', action='append',
default=[], metavar='PHASE1,PHASE2,...',
help='''Comma separated list of seismic phases in classic
nomenclature. Run "cake list-phase-map" for a list of available
phase names. When plotting, color can be specified in the same way
as in --phases.''')
parser.add_option_group(group)
if 'model' in want:
group = OptionGroup(parser, 'Model')
group.add_option(
'--model', dest='model_filename', metavar='(NAME or FILENAME)',
help='Use builtin model named NAME or user model from file '
'FILENAME. By default, the "ak135-f-continental.m" model is '
'used. Run "cake list-models" for a list of builtin models.')
group.add_option(
'--format', dest='model_format', metavar='FORMAT',
choices=['nd', 'hyposat'], default='nd',
help='Set model file format (available: nd, hyposat; default: '
'nd).')
group.add_option(
'--crust2loc', dest='crust2loc', metavar='LAT,LON',
help='Set model from CRUST2.0 profile at location (LAT,LON).')
group.add_option(
'--crust2profile', dest='crust2profile', metavar='KEY',
help='Set model from CRUST2.0 profile with given KEY.')
parser.add_option_group(group)
if any(x in want for x in (
'zstart', 'zstop', 'distances', 'sloc', 'rloc')):
group = OptionGroup(parser, 'Source-receiver geometry')
if 'zstart' in want:
group.add_option(
'--sdepth', dest='sdepth', type='float', default=0.0,
metavar='FLOAT',
help='Source depth [km] (default: 0)')
if 'zstop' in want:
group.add_option(
'--rdepth', dest='rdepth', type='float', default=0.0,
metavar='FLOAT',
help='Receiver depth [km] (default: 0)')
if 'distances' in want:
group.add_option(
'--distances', dest='sdist', metavar='DISTANCES',
help='Surface distances as "start:stop:n" or '
'"dist1,dist2,..." [km]')
group.add_option(
'--sloc', dest='sloc', metavar='LAT,LON',
help='Source location (LAT,LON).')
group.add_option(
'--rloc', dest='rloc', metavar='LAT,LON',
help='Receiver location (LAT,LON).')
parser.add_option_group(group)
if 'material' in want:
group = OptionGroup(
parser, 'Material',
'An isotropic elastic material may be specified by giving '
'a combination of some of the following options. ')
group.add_option(
'--vp', dest='vp', default=None, type='float', metavar='FLOAT',
help='P-wave velocity [km/s]')
group.add_option(
'--vs', dest='vs', default=None, type='float', metavar='FLOAT',
help='S-wave velocity [km/s]')
group.add_option(
'--rho', dest='rho', default=None, type='float', metavar='FLOAT',
help='density [g/cm**3]')
group.add_option(
'--qp', dest='qp', default=None, type='float', metavar='FLOAT',
help='P-wave attenuation Qp (default: 1456)')
group.add_option(
'--qs', dest='qs', default=None, type='float', metavar='FLOAT',
help='S-wave attenuation Qs (default: 600)')
group.add_option(
'--poisson', dest='poisson', default=None, type='float',
metavar='FLOAT',
help='Poisson ratio')
group.add_option(
'--lambda', dest='lame_lambda', default=None, type='float',
metavar='FLOAT',
help='Lame parameter lambda [GPa]')
group.add_option(
'--mu', dest='lame_mu', default=None, type='float',
metavar='FLOAT',
help='Shear modulus [GPa]')
group.add_option(
'--qk', dest='qk', default=None, type='float', metavar='FLOAT',
help='Bulk attenuation Qk')
group.add_option(
'--qmu', dest='qmu', default=None, type='float', metavar='FLOAT',
help='Shear attenuation Qmu')
parser.add_option_group(group)
if any(x in want for x in (
'vred', 'as_degrees', 'accuracy', 'slowness', 'interface',
'aspect', 'shade_model')):
group = OptionGroup(parser, 'General')
if 'vred' in want:
group.add_option(
'--vred', dest='vred', type='float', metavar='FLOAT',
help='Velocity for time reduction in plot [km/s]')
if 'as_degrees' in want:
group.add_option(
'--degrees', dest='as_degrees', action='store_true',
default=False,
help='Distances are in [deg] instead of [km], velocities in '
'[deg/s] instead of [km/s], slownesses in [s/deg] '
'instead of [s/km].')
if 'accuracy' in want:
group.add_option(
'--accuracy', dest='accuracy', type='float',
metavar='MAXIMUM_RELATIVE_RMS', default=0.002,
help='Set accuracy for model simplification.')
if 'slowness' in want:
group.add_option(
'--slowness', dest='slowness', type='float', metavar='FLOAT',
default=0.0,
help='Select surface slowness [s/km] (default: 0)')
if 'interface' in want:
group.add_option(
'--interface', dest='interface', metavar='(NAME or DEPTH)',
help='Name or depth [km] of interface to select')
if 'aspect' in want:
group.add_option(
'--aspect', dest='aspect', type='float', metavar='FLOAT',
help='Aspect ratio for plot')
if 'shade_model' in want:
group.add_option(
'--no-shade-model', dest='shade_model', action='store_false',
default=True,
help='Suppress shading of earth model layers')
parser.add_option_group(group)
if any(x in want for x in ('output_format',)):
group = OptionGroup(parser, 'Output')
if 'output_format' in want:
group.add_option(
'--output-format', dest='output_format', metavar='FORMAT',
default='textual',
choices=('textual', 'nd'),
help='Set model output format (available: textual, nd, '
'default: textual)')
parser.add_option_group(group)
if usage == 'cake help-options':
parser.print_help()
(options, args) = parser.parse_args(args)
if len(args) != 2:
parser.error(
'Cake arguments should look like "--option" or "--option=...".')
d = {}
as_degrees = False
if 'as_degrees' in want:
as_degrees = options.as_degrees
d['as_degrees'] = as_degrees
if 'accuracy' in want:
d['accuracy'] = options.accuracy
if 'output_format' in want:
d['output_format'] = options.output_format
if 'aspect' in want:
d['aspect'] = options.aspect
if 'shade_model' in want:
d['shade_model'] = options.shade_model
if 'phases' in want:
phases = []
phase_colors = {}
try:
for ss in options.phases:
for s in ss.split(','):
s = process_color(s, phase_colors)
phases.append(cake.PhaseDef(s))
for pp in options.classic_phases:
for p in pp.split(','):
p = process_color(p, phase_colors)
phases.extend(cake.PhaseDef.classic(p))
except (cake.PhaseDefParseError, cake.UnknownClassicPhase) as e:
parser.error(e)
if not phases and 'phases' in required:
s = process_color('P', phase_colors)
phases.append(cake.PhaseDef(s))
if phases:
d['phase_colors'] = phase_colors
d['phases'] = phases
if 'model' in want:
if options.model_filename:
d['model'] = cake.load_model(
options.model_filename, options.model_format)
if options.crust2loc or options.crust2profile:
if options.crust2loc:
try:
args = tuple(
[float(x) for x in options.crust2loc.split(',')])
except Exception:
parser.error(
'format for --crust2loc option is '
'"LATITUDE,LONGITUDE"')
elif options.crust2profile:
args = (options.crust2profile.upper(),)
else:
assert False
if 'model' in d:
d['model'] = d['model'].replaced_crust(args)
else:
from pyrocko import crust2x2
profile = crust2x2.get_profile(*args)
d['model'] = cake.LayeredModel.from_scanlines(
cake.from_crust2x2_profile(profile))
if 'vred' in want:
d['vred'] = options.vred
if d['vred'] is not None:
if not as_degrees:
d['vred'] *= r2d * cake.km / cake.earthradius
if 'distances' in want:
distances = None
if options.sdist:
if options.sdist.find(':') != -1:
ssn = options.sdist.split(':')
if len(ssn) != 3:
parser.error(
'format for distances is '
'"min_distance:max_distance:n_distances"')
distances = num.linspace(*map(float, ssn))
else:
distances = num.array(
list(map(
float, options.sdist.split(','))), dtype=num.float)
if not as_degrees:
distances *= r2d * cake.km / cake.earthradius
if options.sloc and options.rloc:
try:
slat, slon = tuple([float(x) for x in options.sloc.split(',')])
rlat, rlon = tuple([float(x) for x in options.rloc.split(',')])
except Exception:
parser.error(
'format for --sloc and --rloc options is '
'"LATITUDE,LONGITUDE"')
distance_sr = orthodrome.distance_accurate50m_numpy(
slat, slon, rlat, rlon)
distance_sr *= r2d / cake.earthradius
if distances is not None:
distances = num.concatenate((distances, [distance_sr]))
else:
distances = num.array([distance_sr], dtype=num.float)
if distances is not None:
d['distances'] = distances
else:
if 'distances' not in required:
d['distances'] = None
if 'slowness' in want:
d['slowness'] = options.slowness/cake.d2r
if not as_degrees:
d['slowness'] /= cake.km*cake.m2d
if 'interface' in want:
if options.interface:
try:
d['interface'] = float(options.interface)*cake.km
except ValueError:
d['interface'] = options.interface
else:
d['interface'] = None
if 'zstart' in want:
d['zstart'] = options.sdepth*cake.km
if 'zstop' in want:
d['zstop'] = options.rdepth*cake.km
if 'material' in want:
md = {}
userfactor = dict(
vp=1000., vs=1000., rho=1000., qp=1., qs=1., qmu=1., qk=1.,
lame_lambda=1.0e9, lame_mu=1.0e9, poisson=1.)
for k in userfactor.keys():
if getattr(options, k) is not None:
md[k] = getattr(options, k) * userfactor[k]
if not (bool('lame_lambda' in md) == bool('lame_mu' in md)):
parser.error('lambda and mu must be specified both.')
if 'lame_lambda' in md and 'lame_mu' in md:
md['lame'] = md.pop('lame_lambda'), md.pop('lame_mu')
if md:
try:
d['material'] = cake.Material(**md)
except cake.InvalidArguments as e:
parser.error(str(e))
for k in list(d.keys()):
if k not in want:
del d[k]
for k in required:
if k not in d:
if k == 'model':
d['model'] = cake.load_model('ak135-f-continental.m')
elif k == 'distances':
d['distances'] = num.linspace(10*cake.km, 100*cake.km, 10) \
/ cake.earthradius * r2d
elif k == 'phases':
d['phases'] = list(map(cake.PhaseDef, 'Pp'))
else:
parser.error('missing %s' % k)
return Anon(d)
def main():
parser = OptionParser(usage=usage, description=description)
parser.add_option('--force',
dest='force',
action='store_true',
default=False,
help='allow recreation of output <directory>')
parser.add_option('--debug',
dest='debug',
action='store_true',
default=False,
help='print debugging information to stderr')
parser.add_option('--dry-run',
dest='dry_run',
action='store_true',
default=False,
help='show available stations/channels and exit '
'(do not download waveforms)')
parser.add_option('--continue',
dest='continue_',
action='store_true',
default=False,
help='continue download after a accident')
parser.add_option('--local-data',
dest='local_data',
action='append',
help='add file/directory with local data')
parser.add_option('--local-stations',
dest='local_stations',
action='append',
help='add local stations file')
parser.add_option('--selection',
dest='selection_file',
action='append',
help='add local stations file')
parser.add_option(
'--local-responses-resp',
dest='local_responses_resp',
action='append',
help='add file/directory with local responses in RESP format')
parser.add_option('--local-responses-pz',
dest='local_responses_pz',
action='append',
help='add file/directory with local pole-zero responses')
parser.add_option(
'--local-responses-stationxml',
dest='local_responses_stationxml',
help='add file with local response information in StationXML format')
parser.add_option(
'--window',
dest='window',
default='full',
help='set time window to choose [full, p, "<time-start>,<time-end>"'
'] (time format is YYYY-MM-DD HH:MM:SS)')
parser.add_option(
'--out-components',
choices=['enu', 'rtu'],
dest='out_components',
default='rtu',
help='set output component orientations to radial-transverse-up [rtu] '
'(default) or east-north-up [enu]')
parser.add_option('--out-units',
choices=['M', 'M/S', 'M/S**2'],
dest='output_units',
default='M',
help='set output units to displacement "M" (default),'
' velocity "M/S" or acceleration "M/S**2"')
parser.add_option(
'--padding-factor',
type=float,
default=3.0,
dest='padding_factor',
help='extend time window on either side, in multiples of 1/<fmin_hz> '
'(default: 5)')
parser.add_option(
'--zero-padding',
dest='zero_pad',
action='store_true',
default=False,
help='Extend traces by zero-padding if clean restitution requires'
'longer windows')
parser.add_option(
'--credentials',
dest='user_credentials',
action='append',
default=[],
metavar='SITE,USER,PASSWD',
help='user credentials for specific site to access restricted data '
'(this option can be repeated)')
parser.add_option(
'--token',
dest='auth_tokens',
metavar='SITE,FILENAME',
action='append',
default=[],
help='user authentication token for specific site to access '
'restricted data (this option can be repeated)')
parser.add_option(
'--sites',
dest='sites',
metavar='SITE1,SITE2,...',
default='geofon,iris,orfeus',
help='sites to query (available: %s, default: "%%default"' %
', '.join(g_sites_available))
parser.add_option(
'--band-codes',
dest='priority_band_code',
metavar='V,L,M,B,H,S,E,...',
default='B,H',
help='select and prioritize band codes (default: %default)')
parser.add_option(
'--instrument-codes',
dest='priority_instrument_code',
metavar='H,L,G,...',
default='H,L',
help='select and prioritize instrument codes (default: %default)')
parser.add_option('--radius-min',
dest='radius_min',
metavar='VALUE',
default=0.0,
type=float,
help='minimum radius [km]')
parser.add_option('--nstations-wanted',
dest='nstations_wanted',
metavar='N',
type=int,
help='number of stations to select initially')
(options, args) = parser.parse_args(sys.argv[1:])
print('Parsed arguments:', args)
if len(args) not in (10, 7, 6):
parser.print_help()
sys.exit(1)
if options.debug:
util.setup_logging(program_name, 'debug')
else:
util.setup_logging(program_name, 'info')
if options.local_responses_pz and options.local_responses_resp:
logger.critical('cannot use local responses in PZ and RESP '
'format at the same time')
sys.exit(1)
n_resp_opt = 0
for resp_opt in (options.local_responses_pz, options.local_responses_resp,
options.local_responses_stationxml):
if resp_opt:
n_resp_opt += 1
if n_resp_opt > 1:
logger.critical('can only handle local responses from either PZ or '
'RESP or StationXML. Cannot yet merge different '
'response formats.')
sys.exit(1)
if options.local_responses_resp and not options.local_stations:
logger.critical('--local-responses-resp can only be used '
'when --stations is also given.')
sys.exit(1)
try:
ename = ''
magnitude = None
mt = None
if len(args) == 10:
time = util.str_to_time(args[1] + ' ' + args[2])
lat = float(args[3])
lon = float(args[4])
depth = float(args[5]) * km
iarg = 6
elif len(args) == 7:
if args[2].find(':') == -1:
sname_or_date = None
lat = float(args[1])
lon = float(args[2])
event = None
time = None
else:
sname_or_date = args[1] + ' ' + args[2]
iarg = 3
elif len(args) == 6:
sname_or_date = args[1]
iarg = 2
if len(args) in (7, 6) and sname_or_date is not None:
events = get_events_by_name_or_date([sname_or_date],
catalog=geofon)
if len(events) == 0:
logger.critical('no event found')
sys.exit(1)
elif len(events) > 1:
logger.critical('more than one event found')
sys.exit(1)
event = events[0]
time = event.time
lat = event.lat
lon = event.lon
depth = event.depth
ename = event.name
magnitude = event.magnitude
mt = event.moment_tensor
radius = float(args[iarg]) * km
fmin = float(args[iarg + 1])
sample_rate = float(args[iarg + 2])
eventname = args[iarg + 3]
cwd = str(sys.argv[1])
event_dir = op.join(cwd, 'data', 'events', eventname)
output_dir = op.join(event_dir, 'waveforms')
except:
raise
parser.print_help()
sys.exit(1)
if options.force and op.isdir(event_dir):
if not options.continue_:
shutil.rmtree(event_dir)
if op.exists(event_dir) and not options.continue_:
logger.critical(
'directory "%s" exists. Delete it first or use the --force option'
% event_dir)
sys.exit(1)
util.ensuredir(output_dir)
if time is not None:
event = model.Event(time=time,
lat=lat,
lon=lon,
depth=depth,
name=ename,
magnitude=magnitude,
moment_tensor=mt)
if options.window == 'full':
if event is None:
logger.critical('need event for --window=full')
sys.exit(1)
low_velocity = 1500.
timewindow = VelocityWindow(low_velocity,
tpad=options.padding_factor / fmin)
tmin, tmax = timewindow(time, radius, depth)
elif options.window == 'p':
if event is None:
logger.critical('need event for --window=p')
sys.exit(1)
phases = list(map(cake.PhaseDef, 'P p'.split()))
emod = cake.load_model()
tpad = options.padding_factor / fmin
timewindow = PhaseWindow(emod, phases, -tpad, tpad)
arrivaltimes = []
for dist in num.linspace(0, radius, 20):
try:
arrivaltimes.extend(timewindow(time, dist, depth))
except NoArrival:
pass
if not arrivaltimes:
logger.error('required phase arrival not found')
sys.exit(1)
tmin = min(arrivaltimes)
tmax = max(arrivaltimes)
else:
try:
stmin, stmax = options.window.split(',')
tmin = util.str_to_time(stmin.strip())
tmax = util.str_to_time(stmax.strip())
timewindow = FixedWindow(tmin, tmax)
except ValueError:
logger.critical('invalid argument to --window: "%s"' %
options.window)
sys.exit(1)
if event is not None:
event.name = eventname
tfade = tfade_factor / fmin
tpad = tfade
tmin -= tpad
tmax += tpad
tinc = None
priority_band_code = options.priority_band_code.split(',')
for s in priority_band_code:
if len(s) != 1:
logger.critical('invalid band code: %s' % s)
priority_instrument_code = options.priority_instrument_code.split(',')
for s in priority_instrument_code:
if len(s) != 1:
logger.critical('invalid instrument code: %s' % s)
station_query_conf = dict(latitude=lat,
longitude=lon,
minradius=options.radius_min * km * cake.m2d,
maxradius=radius * cake.m2d,
channel=','.join('%s??' % s
for s in priority_band_code))
target_sample_rate = sample_rate
fmax = target_sample_rate
# target_sample_rate = None
# priority_band_code = ['H', 'B', 'M', 'L', 'V', 'E', 'S']
priority_units = ['M/S', 'M', 'M/S**2']
# output_units = 'M'
sites = [x.strip() for x in options.sites.split(',') if x.strip()]
for site in sites:
if site not in g_sites_available:
logger.critical('unknown FDSN site: %s' % site)
sys.exit(1)
for s in options.user_credentials:
try:
site, user, passwd = s.split(',')
g_user_credentials[site] = user, passwd
except ValueError:
logger.critical('invalid format for user credentials: "%s"' % s)
sys.exit(1)
for s in options.auth_tokens:
try:
site, token_filename = s.split(',')
with open(token_filename, 'r') as f:
g_auth_tokens[site] = f.read()
except (ValueError, OSError, IOError):
logger.critical('cannot get token from file: %s' % token_filename)
sys.exit(1)
fn_template0 = \
'data_%(network)s.%(station)s.%(location)s.%(channel)s_%(tmin)s.mseed'
fn_template_raw = op.join(output_dir, 'raw', fn_template0)
fn_stations_raw = op.join(output_dir, 'stations.raw.txt')
fn_template_rest = op.join(output_dir, 'rest', fn_template0)
fn_commandline = op.join(output_dir, 'beatdown.command')
ftap = (ffade_factors[0] * fmin, fmin, fmax, ffade_factors[1] * fmax)
# chapter 1: download
sxs = []
for site in sites:
try:
extra_args = {
'iris': dict(matchtimeseries=True),
}.get(site, {})
extra_args.update(station_query_conf)
if site == 'geonet':
extra_args.update(starttime=tmin, endtime=tmax)
else:
extra_args.update(startbefore=tmax,
endafter=tmin,
includerestricted=(site in g_user_credentials
or site in g_auth_tokens))
logger.info('downloading channel information (%s)' % site)
sx = fdsn.station(site=site,
format='text',
level='channel',
**extra_args)
except fdsn.EmptyResult:
logger.error('No stations matching given criteria. (%s)' % site)
sx = None
if sx is not None:
sxs.append(sx)
if all(sx is None for sx in sxs) and not options.local_data:
sys.exit(1)
nsl_to_sites = defaultdict(list)
nsl_to_station = {}
if options.selection_file:
logger.info('using stations from stations file!')
stations = []
for fn in options.selection_file:
stations.extend(model.load_stations(fn))
nsls_selected = set(s.nsl() for s in stations)
else:
nsls_selected = None
for sx, site in zip(sxs, sites):
site_stations = sx.get_pyrocko_stations()
for s in site_stations:
nsl = s.nsl()
nsl_to_sites[nsl].append(site)
if nsl not in nsl_to_station:
if nsls_selected:
if nsl in nsls_selected:
nsl_to_station[nsl] = s
else:
nsl_to_station[
nsl] = s # using first site with this station
logger.info('number of stations found: %i' % len(nsl_to_station))
# station weeding
if options.nstations_wanted:
nsls_selected = None
stations_all = [
nsl_to_station[nsl_] for nsl_ in sorted(nsl_to_station.keys())
]
for s in stations_all:
s.set_event_relative_data(event)
stations_selected = weeding.weed_stations(stations_all,
options.nstations_wanted)[0]
nsls_selected = set(s.nsl() for s in stations_selected)
logger.info('number of stations selected: %i' % len(nsls_selected))
if tinc is None:
tinc = 3600.
have_data = set()
if options.continue_:
fns = glob.glob(fn_template_raw % starfill())
p = pile.make_pile(fns)
else:
fns = []
have_data_site = {}
could_have_data_site = {}
for site in sites:
have_data_site[site] = set()
could_have_data_site[site] = set()
available_through = defaultdict(set)
it = 0
nt = int(math.ceil((tmax - tmin) / tinc))
for it in range(nt):
tmin_win = tmin + it * tinc
tmax_win = min(tmin + (it + 1) * tinc, tmax)
logger.info('time window %i/%i (%s - %s)' %
(it + 1, nt, util.tts(tmin_win), util.tts(tmax_win)))
have_data_this_window = set()
if options.continue_:
trs_avail = p.all(tmin=tmin_win, tmax=tmax_win, load_data=False)
for tr in trs_avail:
have_data_this_window.add(tr.nslc_id)
for site, sx in zip(sites, sxs):
if sx is None:
continue
selection = []
channels = sx.choose_channels(
target_sample_rate=target_sample_rate,
priority_band_code=priority_band_code,
priority_units=priority_units,
priority_instrument_code=priority_instrument_code,
timespan=(tmin_win, tmax_win))
for nslc in sorted(channels.keys()):
if nsls_selected is not None and nslc[:3] not in nsls_selected:
continue
could_have_data_site[site].add(nslc)
if nslc not in have_data_this_window:
channel = channels[nslc]
if event:
lat_, lon_ = event.lat, event.lon
else:
lat_, lon_ = lat, lon
try:
dist = orthodrome.distance_accurate50m_numpy(
lat_, lon_, channel.latitude.value,
channel.longitude.value)
except:
dist = orthodrome.distance_accurate50m_numpy(
lat_, lon_, channel.latitude, channel.longitude)
if event:
depth_ = event.depth
time_ = event.time
else:
depth_ = None
time_ = None
tmin_, tmax_ = timewindow(time_, dist, depth_)
tmin_this = tmin_ - tpad
tmax_this = float(tmax_ + tpad)
tmin_req = max(tmin_win, tmin_this)
tmax_req = min(tmax_win, tmax_this)
if channel.sample_rate:
try:
deltat = 1.0 / int(channel.sample_rate.value)
except:
deltat = 1.0 / int(channel.sample_rate)
else:
deltat = 1.0
if tmin_req < tmax_req:
logger.debug('deltat %f' % deltat)
# extend time window by some samples because otherwise
# sometimes gaps are produced
# apparently the WS are only sensitive to full seconds
# round to avoid gaps, increase safetiy window
selection.append(nslc +
(math.floor(tmin_req - deltat * 20.0),
math.ceil(tmax_req + deltat * 20.0)))
if options.dry_run:
for (net, sta, loc, cha, tmin, tmax) in selection:
available_through[net, sta, loc, cha].add(site)
else:
neach = 100
i = 0
nbatches = ((len(selection) - 1) // neach) + 1
while i < len(selection):
selection_now = selection[i:i + neach]
f = tempfile.NamedTemporaryFile()
try:
sbatch = ''
if nbatches > 1:
sbatch = ' (batch %i/%i)' % (
(i // neach) + 1, nbatches)
logger.info('downloading data (%s)%s' % (site, sbatch))
data = fdsn.dataselect(site=site,
selection=selection_now,
**get_user_credentials(site))
while True:
buf = data.read(1024)
if not buf:
break
f.write(buf)
f.flush()
trs = io.load(f.name)
for tr in trs:
tr.fix_deltat_rounding_errors()
logger.debug('cutting window: %f - %f' %
(tmin_win, tmax_win))
logger.debug(
'available window: %f - %f, nsamples: %g' %
(tr.tmin, tr.tmax, tr.ydata.size))
try:
logger.debug('tmin before snap %f' % tr.tmin)
tr.snap(interpolate=True)
logger.debug('tmin after snap %f' % tr.tmin)
tr.chop(tmin_win,
tmax_win,
snap=(math.floor, math.ceil),
include_last=True)
logger.debug(
'cut window: %f - %f, nsamles: %g' %
(tr.tmin, tr.tmax, tr.ydata.size))
have_data.add(tr.nslc_id)
have_data_site[site].add(tr.nslc_id)
except trace.NoData:
pass
fns2 = io.save(trs, fn_template_raw)
for fn in fns2:
if fn in fns:
logger.warn('overwriting file %s', fn)
fns.extend(fns2)
except fdsn.EmptyResult:
pass
except HTTPError:
logger.warn('an error occurred while downloading data '
'for channels \n %s' %
'\n '.join('.'.join(x[:4])
for x in selection_now))
f.close()
i += neach
if options.dry_run:
nslcs = sorted(available_through.keys())
all_channels = defaultdict(set)
all_stations = defaultdict(set)
def plural_s(x):
return '' if x == 1 else 's'
for nslc in nslcs:
sites = tuple(sorted(available_through[nslc]))
logger.info('selected: %s.%s.%s.%s from site%s %s' %
(nslc + (plural_s(len(sites)), '+'.join(sites))))
all_channels[sites].add(nslc)
all_stations[sites].add(nslc[:3])
nchannels_all = 0
nstations_all = 0
for sites in sorted(all_channels.keys(),
key=lambda sites: (-len(sites), sites)):
nchannels = len(all_channels[sites])
nstations = len(all_stations[sites])
nchannels_all += nchannels
nstations_all += nstations
logger.info('selected (%s): %i channel%s (%i station%s)' %
('+'.join(sites), nchannels, plural_s(nchannels),
nstations, plural_s(nstations)))
logger.info('selected total: %i channel%s (%i station%s)' %
(nchannels_all, plural_s(nchannels_all), nstations_all,
plural_s(nstations_all)))
logger.info('dry run done.')
sys.exit(0)
for nslc in have_data:
# if we are in continue mode, we have to guess where the data came from
if not any(nslc in have_data_site[site] for site in sites):
for site in sites:
if nslc in could_have_data_site[site]:
have_data_site[site].add(nslc)
sxs = {}
for site in sites:
selection = []
for nslc in sorted(have_data_site[site]):
selection.append(nslc + (tmin - tpad, tmax + tpad))
if selection:
logger.info('downloading response information (%s)' % site)
sxs[site] = fdsn.station(site=site,
level='response',
selection=selection)
sxs[site].dump_xml(filename=op.join(output_dir, 'stations.%s.xml' %
site))
# chapter 1.5: inject local data
if options.local_data:
have_data_site['local'] = set()
plocal = pile.make_pile(options.local_data, fileformat='detect')
logger.info(
'Importing local data from %s between %s (%f) and %s (%f)' %
(options.local_data, util.time_to_str(tmin), tmin,
util.time_to_str(tmax), tmax))
for traces in plocal.chopper_grouped(gather=lambda tr: tr.nslc_id,
tmin=tmin,
tmax=tmax,
tinc=tinc):
for tr in traces:
if tr.nslc_id not in have_data:
fns.extend(io.save(traces, fn_template_raw))
have_data_site['local'].add(tr.nslc_id)
have_data.add(tr.nslc_id)
sites.append('local')
if options.local_responses_pz:
sxs['local'] = epz.make_stationxml(
epz.iload(options.local_responses_pz))
if options.local_responses_resp:
local_stations = []
for fn in options.local_stations:
local_stations.extend(model.load_stations(fn))
sxs['local'] = resp.make_stationxml(
local_stations, resp.iload(options.local_responses_resp))
if options.local_responses_stationxml:
sxs['local'] = stationxml.load_xml(
filename=options.local_responses_stationxml)
# chapter 1.6: dump raw data stations file
nsl_to_station = {}
for site in sites:
if site in sxs:
stations = sxs[site].get_pyrocko_stations(timespan=(tmin, tmax))
for s in stations:
nsl = s.nsl()
if nsl not in nsl_to_station:
nsl_to_station[nsl] = s
stations = [nsl_to_station[nsl_] for nsl_ in sorted(nsl_to_station.keys())]
util.ensuredirs(fn_stations_raw)
model.dump_stations(stations, fn_stations_raw)
dump_commandline(sys.argv, fn_commandline)
# chapter 2: restitution
if not fns:
logger.error('no data available')
sys.exit(1)
p = pile.make_pile(fns, show_progress=False)
p.get_deltatmin()
otinc = None
if otinc is None:
otinc = nice_seconds_floor(p.get_deltatmin() * 500000.)
otinc = 3600.
otmin = math.floor(p.tmin / otinc) * otinc
otmax = math.ceil(p.tmax / otinc) * otinc
otpad = tpad * 2
fns = []
rest_traces_b = []
win_b = None
for traces_a in p.chopper_grouped(gather=lambda tr: tr.nslc_id,
tmin=otmin,
tmax=otmax,
tinc=otinc,
tpad=otpad):
rest_traces_a = []
win_a = None
for tr in traces_a:
win_a = tr.wmin, tr.wmax
if win_b and win_b[0] >= win_a[0]:
fns.extend(cut_n_dump(rest_traces_b, win_b, fn_template_rest))
rest_traces_b = []
win_b = None
response = None
failure = []
for site in sites:
try:
if site not in sxs:
continue
logger.debug('Getting response for %s' % tr.__str__())
response = sxs[site].get_pyrocko_response(
tr.nslc_id,
timespan=(tr.tmin, tr.tmax),
fake_input_units=options.output_units)
break
except stationxml.NoResponseInformation:
failure.append('%s: no response information' % site)
except stationxml.MultipleResponseInformation:
failure.append('%s: multiple response information' % site)
if response is None:
failure = ', '.join(failure)
else:
failure = ''
try:
if tr.tmin > tmin and options.zero_pad:
logger.warning(
'Trace too short for clean restitution in '
'desired frequency band -> zero-padding!')
tr.extend(tr.tmin - tfade, tr.tmax + tfade, 'repeat')
rest_tr = tr.transfer(tfade, ftap, response, invert=True)
rest_traces_a.append(rest_tr)
except (trace.TraceTooShort, trace.NoData):
failure = 'trace too short'
if failure:
logger.warn('failed to restitute trace %s.%s.%s.%s (%s)' %
(tr.nslc_id + (failure, )))
if rest_traces_b:
rest_traces = trace.degapper(rest_traces_b + rest_traces_a,
deoverlap='crossfade_cos')
fns.extend(cut_n_dump(rest_traces, win_b, fn_template_rest))
rest_traces_a = []
if win_a:
for tr in rest_traces:
try:
rest_traces_a.append(
tr.chop(win_a[0], win_a[1] + otpad, inplace=False))
except trace.NoData:
pass
rest_traces_b = rest_traces_a
win_b = win_a
fns.extend(cut_n_dump(rest_traces_b, win_b, fn_template_rest))
# chapter 3: rotated restituted traces for inspection
if not event:
sys.exit(0)
fn_template1 = \
'DISPL.%(network)s.%(station)s.%(location)s.%(channel)s'
fn_waveforms = op.join(output_dir, 'prepared', fn_template1)
fn_stations = op.join(output_dir, 'stations.prepared.txt')
fn_event = op.join(event_dir, 'event.txt')
fn_event_yaml = op.join(event_dir, 'event.yaml')
nsl_to_station = {}
for site in sites:
if site in sxs:
stations = sxs[site].get_pyrocko_stations(timespan=(tmin, tmax))
for s in stations:
nsl = s.nsl()
if nsl not in nsl_to_station:
nsl_to_station[nsl] = s
p = pile.make_pile(fns, show_progress=False)
deltat = None
if sample_rate is not None:
deltat = 1.0 / sample_rate
traces_beat = []
used_stations = []
for nsl, s in nsl_to_station.items():
s.set_event_relative_data(event)
traces = p.all(trace_selector=lambda tr: tr.nslc_id[:3] == nsl)
if options.out_components == 'rtu':
pios = s.guess_projections_to_rtu(out_channels=('R', 'T', 'Z'))
elif options.out_components == 'enu':
pios = s.guess_projections_to_enu(out_channels=('E', 'N', 'Z'))
else:
assert False
for (proj, in_channels, out_channels) in pios:
proc = trace.project(traces, proj, in_channels, out_channels)
for tr in proc:
tr_beat = heart.SeismicDataset.from_pyrocko_trace(tr)
traces_beat.append(tr_beat)
for ch in out_channels:
if ch.name == tr.channel:
s.add_channel(ch)
if proc:
io.save(proc, fn_waveforms)
used_stations.append(s)
stations = list(used_stations)
util.ensuredirs(fn_stations)
model.dump_stations(stations, fn_stations)
model.dump_events([event], fn_event)
from pyrocko.guts import dump
dump([event], filename=fn_event_yaml)
utility.dump_objects(op.join(cwd, 'seismic_data.pkl'),
outlist=[stations, traces_beat])
logger.info('prepared waveforms from %i stations' % len(stations))
import numpy as num from pyrocko import orthodrome ncoords = 1000 # First set of coordinates lats_a = num.random.uniform(-180., 180., ncoords) lons_a = num.random.uniform(-90., 90., ncoords) # Second set of coordinates lats_b = num.random.uniform(-180., 180., ncoords) lons_b = num.random.uniform(-90., 90., ncoords) orthodrome.distance_accurate50m_numpy(lats_a, lons_a, lats_b, lons_b)
def call(self):
if not self.viewer_connected:
self.get_viewer().about_to_close.connect(
self.file_serving_worker.stop)
self.viewer_connected = True
try:
from OpenGL import GL # noqa
except ImportError:
logger.warn(
'Could not find package OpenGL, '
'if the map does not work try installing OpenGL\n'
'e.g. sudo pip install PyOpenGL')
self.cleanup()
try:
viewer = self.get_viewer()
cli_mode = False
except NoViewerSet:
viewer = None
cli_mode = True
if not cli_mode:
if self.only_active:
_, active_stations = \
self.get_active_event_and_stations()
else:
active_stations = viewer.stations.values()
elif cli_mode:
active_stations = self.stations
station_list = []
if active_stations:
for stat in active_stations:
is_blacklisted = util.match_nslc(viewer.blacklist, stat.nsl())
if (viewer and not is_blacklisted) or cli_mode:
xml_station_marker = XMLStationMarker(
nsl='.'.join(stat.nsl()),
longitude=float(stat.lon),
latitude=float(stat.lat),
active='yes')
station_list.append(xml_station_marker)
active_station_list = StationMarkerList(stations=station_list)
if self.only_active:
markers = [viewer.get_active_event_marker()]
else:
if cli_mode:
markers = self.markers
else:
markers = self.get_selected_markers()
if len(markers) == 0:
tmin, tmax = self.get_selected_time_range(fallback=True)
markers = [m for m in viewer.get_markers()
if isinstance(m, gui_util.EventMarker) and
m.tmin >= tmin and m.tmax <= tmax]
ev_marker_list = []
for m in markers:
if not isinstance(m, gui_util.EventMarker):
continue
xmleventmarker = convert_event_marker(m)
if xmleventmarker is None:
continue
ev_marker_list.append(xmleventmarker)
event_list = EventMarkerList(events=ev_marker_list)
event_station_list = MarkerLists(
station_marker_list=active_station_list,
event_marker_list=event_list)
event_station_list.validate()
if self.map_kind != 'GMT':
tempdir = self.marker_tempdir
if self.map_kind == 'Google Maps':
map_fn = 'map_googlemaps.html'
elif self.map_kind == 'OpenStreetMap':
map_fn = 'map_osm.html'
url = 'http://localhost:' + str(self.port) + '/%s' % map_fn
files = ['loadxmldoc.js', 'map_util.js', 'plates.kml', map_fn]
snuffling_dir = op.dirname(op.abspath(__file__))
for entry in files:
shutil.copy(os.path.join(snuffling_dir, entry),
os.path.join(tempdir, entry))
logger.debug('copied data to %s' % tempdir)
markers_fn = os.path.join(self.marker_tempdir, 'markers.xml')
self.data_proxy.content_to_serve.emit(self.port)
dump_xml(event_station_list, filename=markers_fn)
if self.open_external:
qg.QDesktopServices.openUrl(qc.QUrl(url))
else:
global g_counter
g_counter += 1
self.web_frame(
url,
name='Map %i (%s)' % (g_counter, self.map_kind))
else:
lats_all = []
lons_all = []
slats = []
slons = []
slabels = []
for s in active_stations:
slats.append(s.lat)
slons.append(s.lon)
slabels.append('.'.join(s.nsl()))
elats = []
elons = []
elats = []
elons = []
psmeca_input = []
markers = self.get_selected_markers()
for m in markers:
if isinstance(m, gui_util.EventMarker):
e = m.get_event()
elats.append(e.lat)
elons.append(e.lon)
if e.moment_tensor is not None:
mt = e.moment_tensor.m6()
psmeca_input.append(
(e.lon, e.lat, e.depth/1000., mt[0], mt[1],
mt[2], mt[3], mt[4], mt[5],
1., e.lon, e.lat, e.name))
else:
if e.magnitude is None:
moment = -1.
else:
moment = moment_tensor.magnitude_to_moment(
e.magnitude)
psmeca_input.append(
(e.lon, e.lat, e.depth/1000.,
moment/3., moment/3., moment/3.,
0., 0., 0., 1., e.lon, e.lat, e.name))
lats_all.extend(elats)
lons_all.extend(elons)
lats_all.extend(slats)
lons_all.extend(slons)
lats_all = num.array(lats_all)
lons_all = num.array(lons_all)
if len(lats_all) == 0:
return
center_lat, center_lon = ortho.geographic_midpoint(
lats_all, lons_all)
ntotal = len(lats_all)
clats = num.ones(ntotal) * center_lat
clons = num.ones(ntotal) * center_lon
dists = ortho.distance_accurate50m_numpy(
clats, clons, lats_all, lons_all)
maxd = num.max(dists) or 0.
m = Map(
lat=center_lat, lon=center_lon,
radius=max(10000., maxd) * 1.1,
width=35, height=25,
show_grid=True,
show_topo=True,
color_dry=(238, 236, 230),
topo_cpt_wet='light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=False,
show_plates=False)
m.gmt.psxy(in_columns=(slons, slats), S='t15p', G='black', *m.jxyr)
for i in range(len(active_stations)):
m.add_label(slats[i], slons[i], slabels[i])
m.gmt.psmeca(
in_rows=psmeca_input, S='m1.0', G='red', C='5p,0/0/0', *m.jxyr)
tmpdir = self.tempdir()
self.outfn = os.path.join(tmpdir, '%i.png' % self.figcount)
m.save(self.outfn)
f = self.pixmap_frame(self.outfn) # noqa
def update_distances_and_angles(self, indices=None, want_angles=False,
want_distances=False):
'''Calculate and update distances and kagan angles between events.
:param indices: list of lists of indices (optional)
Ideally, indices are consecutive for best performance.'''
want_angles = want_angles or \
not self.marker_table_view.isColumnHidden(
_column_mapping['Kagan Angle [deg]'])
want_distances = want_distances or \
not self.marker_table_view.isColumnHidden(
_column_mapping['Dist [km]'])
if not (want_distances or want_angles):
return
indices = indices or [[]]
indices = [i for ii in indices for i in ii]
if len(indices) != 1:
return
if self.last_active_event == self.pile_viewer.get_active_event():
return
else:
self.last_active_event = self.pile_viewer.get_active_event()
markers = self.pile_viewer.markers
nmarkers = len(markers)
omarker = markers[indices[0]]
if not isinstance(omarker, EventMarker):
return
else:
oevent = omarker.get_event()
emarkers = [m for m in markers if isinstance(m, EventMarker)]
if len(emarkers) < 2:
return
else:
events = [em.get_event() for em in emarkers]
nevents = len(events)
if want_distances:
lats = num.zeros(nevents)
lons = num.zeros(nevents)
for i in range(nevents):
lats[i] = events[i].lat
lons[i] = events[i].lon
olats = num.zeros(nevents)
olons = num.zeros(nevents)
olats[:] = oevent.lat
olons[:] = oevent.lon
dists = orthodrome.distance_accurate50m_numpy(
lats, lons, olats, olons)
dists /= 1000.
dists = [round(x, 1) for x in dists]
self.distances = dict(list(zip(emarkers, dists)))
if want_angles:
if oevent.moment_tensor:
for em in emarkers:
e = em.get_event()
if e.moment_tensor:
a = kagan_angle(oevent.moment_tensor, e.moment_tensor)
self.kagan_angles[em] = a
else:
self.kagan_angles = {}
istart = self.index(0, _column_mapping['Dist [km]'])
istop = self.index(nmarkers-1, _column_mapping['Kagan Angle [deg]'])
self.dataChanged.emit(
istart,
istop)
def call(self):
if not self.viewer_connected:
self.get_viewer().about_to_close.connect(
self.file_serving_worker.stop)
self.viewer_connected = True
try:
from OpenGL import GL # noqa
except ImportError:
logger.warn('Could not find package OpenGL, '
'if the map does not work try installing OpenGL\n'
'e.g. sudo pip install PyOpenGL')
self.cleanup()
try:
viewer = self.get_viewer()
cli_mode = False
except NoViewerSet:
viewer = None
cli_mode = True
if not cli_mode:
if self.only_active:
_, active_stations = \
self.get_active_event_and_stations()
else:
active_stations = viewer.stations.values()
elif cli_mode:
active_stations = self.stations
station_list = []
if active_stations:
for stat in active_stations:
is_blacklisted = util.match_nslc(viewer.blacklist, stat.nsl())
if (viewer and not is_blacklisted) or cli_mode:
xml_station_marker = XMLStationMarker(
nsl='.'.join(stat.nsl()),
longitude=float(stat.lon),
latitude=float(stat.lat),
active='yes')
station_list.append(xml_station_marker)
active_station_list = StationMarkerList(stations=station_list)
if self.only_active:
markers = [viewer.get_active_event_marker()]
else:
if cli_mode:
markers = self.markers
else:
markers = self.get_selected_markers()
if len(markers) == 0:
tmin, tmax = self.get_selected_time_range(fallback=True)
markers = [
m for m in viewer.get_markers()
if isinstance(m, gui_util.EventMarker)
and m.tmin >= tmin and m.tmax <= tmax
]
ev_marker_list = []
for m in markers:
if not isinstance(m, gui_util.EventMarker):
continue
xmleventmarker = convert_event_marker(m)
if xmleventmarker is None:
continue
ev_marker_list.append(xmleventmarker)
event_list = EventMarkerList(events=ev_marker_list)
event_station_list = MarkerLists(
station_marker_list=active_station_list,
event_marker_list=event_list)
event_station_list.validate()
if self.map_kind != 'GMT':
tempdir = self.marker_tempdir
if self.map_kind == 'Google Maps':
map_fn = 'map_googlemaps.html'
elif self.map_kind == 'OpenStreetMap':
map_fn = 'map_osm.html'
url = 'http://localhost:' + str(self.port) + '/%s' % map_fn
files = ['loadxmldoc.js', 'map_util.js', 'plates.kml', map_fn]
snuffling_dir = op.dirname(op.abspath(__file__))
for entry in files:
shutil.copy(os.path.join(snuffling_dir, entry),
os.path.join(tempdir, entry))
logger.debug('copied data to %s' % tempdir)
markers_fn = os.path.join(self.marker_tempdir, 'markers.xml')
self.data_proxy.content_to_serve.emit(self.port)
dump_xml(event_station_list, filename=markers_fn)
if self.open_external:
qg.QDesktopServices.openUrl(qc.QUrl(url))
else:
global g_counter
g_counter += 1
self.web_frame(url,
name='Map %i (%s)' % (g_counter, self.map_kind))
else:
lats_all = []
lons_all = []
slats = []
slons = []
slabels = []
for s in active_stations:
slats.append(s.lat)
slons.append(s.lon)
slabels.append('.'.join(s.nsl()))
elats = []
elons = []
elats = []
elons = []
psmeca_input = []
markers = self.get_selected_markers()
for m in markers:
if isinstance(m, gui_util.EventMarker):
e = m.get_event()
elats.append(e.lat)
elons.append(e.lon)
if e.moment_tensor is not None:
mt = e.moment_tensor.m6()
psmeca_input.append((e.lon, e.lat, e.depth / 1000.,
mt[0], mt[1], mt[2], mt[3], mt[4],
mt[5], 1., e.lon, e.lat, e.name))
else:
if e.magnitude is None:
moment = -1.
else:
moment = moment_tensor.magnitude_to_moment(
e.magnitude)
psmeca_input.append(
(e.lon, e.lat, e.depth / 1000., moment / 3.,
moment / 3., moment / 3., 0., 0., 0., 1.,
e.lon, e.lat, e.name))
lats_all.extend(elats)
lons_all.extend(elons)
lats_all.extend(slats)
lons_all.extend(slons)
lats_all = num.array(lats_all)
lons_all = num.array(lons_all)
if len(lats_all) == 0:
return
center_lat, center_lon = ortho.geographic_midpoint(
lats_all, lons_all)
ntotal = len(lats_all)
clats = num.ones(ntotal) * center_lat
clons = num.ones(ntotal) * center_lon
dists = ortho.distance_accurate50m_numpy(clats, clons, lats_all,
lons_all)
maxd = num.max(dists) or 0.
m = Map(lat=center_lat,
lon=center_lon,
radius=max(10000., maxd) * 1.1,
width=35,
height=25,
show_grid=True,
show_topo=True,
color_dry=(238, 236, 230),
topo_cpt_wet='light_sea_uniform',
topo_cpt_dry='light_land_uniform',
illuminate=True,
illuminate_factor_ocean=0.15,
show_rivers=False,
show_plates=False)
m.gmt.psxy(in_columns=(slons, slats), S='t15p', G='black', *m.jxyr)
for i in range(len(active_stations)):
m.add_label(slats[i], slons[i], slabels[i])
m.gmt.psmeca(in_rows=psmeca_input,
S='m1.0',
G='red',
C='5p,0/0/0',
*m.jxyr)
tmpdir = self.tempdir()
self.outfn = os.path.join(tmpdir, '%i.png' % self.figcount)
m.save(self.outfn)
f = self.pixmap_frame(self.outfn) # noqa
def main(args=None):
if args is None:
args = sys.argv[1:]
parser = OptionParser(
usage=usage,
description=description)
parser.add_option(
'--width',
dest='width',
type='float',
default=20.0,
metavar='FLOAT',
help='set width of output image [cm] (%default)')
parser.add_option(
'--height',
dest='height',
type='float',
default=15.0,
metavar='FLOAT',
help='set height of output image [cm] (%default)')
parser.add_option(
'--topo-resolution-min',
dest='topo_resolution_min',
type='float',
default=40.0,
metavar='FLOAT',
help='minimum resolution of topography [dpi] (%default)')
parser.add_option(
'--topo-resolution-max',
dest='topo_resolution_max',
type='float',
default=200.0,
metavar='FLOAT',
help='maximum resolution of topography [dpi] (%default)')
parser.add_option(
'--no-grid',
dest='show_grid',
default=True,
action='store_false',
help='don\'t show grid lines')
parser.add_option(
'--no-topo',
dest='show_topo',
default=True,
action='store_false',
help='don\'t show topography')
parser.add_option(
'--no-cities',
dest='show_cities',
default=True,
action='store_false',
help='don\'t show cities')
parser.add_option(
'--no-illuminate',
dest='illuminate',
default=True,
action='store_false',
help='deactivate artificial illumination of topography')
parser.add_option(
'--illuminate-factor-land',
dest='illuminate_factor_land',
type='float',
metavar='FLOAT',
help='set factor for artificial illumination of land (0.5)')
parser.add_option(
'--illuminate-factor-ocean',
dest='illuminate_factor_ocean',
type='float',
metavar='FLOAT',
help='set factor for artificial illumination of ocean (0.25)')
parser.add_option(
'--theme',
choices=['topo', 'soft'],
default='topo',
help='select color theme, available: topo, soft (topo)"')
parser.add_option(
'--download-etopo1',
dest='download_etopo1',
action='store_true',
help='download full ETOPO1 topography dataset')
parser.add_option(
'--download-srtmgl3',
dest='download_srtmgl3',
action='store_true',
help='download full SRTMGL3 topography dataset')
parser.add_option(
'--make-decimated-topo',
dest='make_decimated',
action='store_true',
help='pre-make all decimated topography datasets')
parser.add_option(
'--stations',
dest='stations_fn',
metavar='FILENAME',
help='load station coordinates from FILENAME')
parser.add_option(
'--events',
dest='events_fn',
metavar='FILENAME',
help='load event coordinates from FILENAME')
parser.add_option(
'--debug',
dest='debug',
action='store_true',
default=False,
help='print debugging information to stderr')
(options, args) = parser.parse_args(args)
if options.debug:
util.setup_logging(program_name, 'debug')
else:
util.setup_logging(program_name, 'info')
if options.download_etopo1:
import pyrocko.datasets.topo.etopo1
pyrocko.datasets.topo.etopo1.download()
if options.download_srtmgl3:
import pyrocko.datasets.topo.srtmgl3
pyrocko.datasets.topo.srtmgl3.download()
if options.make_decimated:
import pyrocko.datasets.topo
pyrocko.datasets.topo.make_all_missing_decimated()
if (options.download_etopo1 or options.download_srtmgl3 or
options.make_decimated) and len(args) == 0:
sys.exit(0)
if options.theme == 'soft':
color_kwargs = {
'illuminate_factor_land': options.illuminate_factor_land or 0.2,
'illuminate_factor_ocean': options.illuminate_factor_ocean or 0.15,
'color_wet': (216, 242, 254),
'color_dry': (238, 236, 230),
'topo_cpt_wet': 'light_sea_uniform',
'topo_cpt_dry': 'light_land_uniform'}
elif options.theme == 'topo':
color_kwargs = {
'illuminate_factor_land': options.illuminate_factor_land or 0.5,
'illuminate_factor_ocean': options.illuminate_factor_ocean or 0.25}
events = []
if options.events_fn:
events = model.load_events(options.events_fn)
stations = []
if options.stations_fn:
stations = model.load_stations(options.stations_fn)
if not (len(args) == 4 or (
len(args) == 1 and (events or stations))):
parser.print_help()
sys.exit(1)
if len(args) == 4:
try:
lat = float(args[0])
lon = float(args[1])
radius = float(args[2])*km
except Exception:
parser.print_help()
sys.exit(1)
else:
lats, lons = latlon_arrays(stations+events)
lat, lon = map(float, od.geographic_midpoint(lats, lons))
radius = float(
num.max(od.distance_accurate50m_numpy(lat, lon, lats, lons)))
radius *= 1.1
m = automap.Map(
width=options.width,
height=options.height,
lat=lat,
lon=lon,
radius=radius,
topo_resolution_max=options.topo_resolution_max,
topo_resolution_min=options.topo_resolution_min,
show_topo=options.show_topo,
show_grid=options.show_grid,
illuminate=options.illuminate,
**color_kwargs)
logger.debug('map configuration:\n%s' % str(m))
if options.show_cities:
m.draw_cities()
if stations:
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
m.gmt.psxy(
in_columns=(lons, lats),
S='t8p',
G='black',
*m.jxyr)
for s in stations:
m.add_label(s.lat, s.lon, '%s' % '.'.join(
x for x in s.nsl() if x))
if events:
beachball_symbol = 'mt'
beachball_size = 20.0
for ev in events:
if ev.moment_tensor is None:
m.gmt.psxy(
in_rows=[[ev.lon, ev.lat]],
S='c12p',
G=gmtpy.color('scarletred2'),
W='1p,black',
*m.jxyr)
else:
devi = ev.moment_tensor.deviatoric()
mt = devi.m_up_south_east()
mt = mt / ev.moment_tensor.scalar_moment() \
* pmt.magnitude_to_moment(5.0)
m6 = pmt.to6(mt)
data = (ev.lon, ev.lat, 10) + tuple(m6) + (1, 0, 0)
if m.gmt.is_gmt5():
kwargs = dict(
M=True,
S='%s%g' % (
beachball_symbol[0], beachball_size / gmtpy.cm))
else:
kwargs = dict(
S='%s%g' % (
beachball_symbol[0], beachball_size*2 / gmtpy.cm))
m.gmt.psmeca(
in_rows=[data],
G=gmtpy.color('chocolate1'),
E='white',
W='1p,%s' % gmtpy.color('chocolate3'),
*m.jxyr,
**kwargs)
m.save(args[-1])
def main():
parser = OptionParser(usage=usage, description=description)
parser.add_option(
"--force",
dest="force",
action="store_true",
default=False,
help="allow recreation of output <directory>",
)
parser.add_option(
"--debug",
dest="debug",
action="store_true",
default=False,
help="print debugging information to stderr",
)
parser.add_option(
"--dry-run",
dest="dry_run",
action="store_true",
default=False,
help="show available stations/channels and exit "
"(do not download waveforms)",
)
parser.add_option(
"--continue",
dest="continue_",
action="store_true",
default=False,
help="continue download after a accident",
)
parser.add_option(
"--local-data",
dest="local_data",
action="append",
help="add file/directory with local data",
)
parser.add_option(
"--local-stations",
dest="local_stations",
action="append",
help="add local stations file",
)
parser.add_option(
"--local-responses-resp",
dest="local_responses_resp",
action="append",
help="add file/directory with local responses in RESP format",
)
parser.add_option(
"--local-responses-pz",
dest="local_responses_pz",
action="append",
help="add file/directory with local pole-zero responses",
)
parser.add_option(
"--local-responses-stationxml",
dest="local_responses_stationxml",
help="add file with local response information in StationXML format",
)
parser.add_option(
"--window",
dest="window",
default="full",
help='set time window to choose [full, p, "<time-start>,<time-end>"'
"] (time format is YYYY-MM-DD HH:MM:SS)",
)
parser.add_option(
"--out-components",
choices=["enu", "rtu"],
dest="out_components",
default="rtu",
help="set output component orientations to radial-transverse-up [rtu] "
"(default) or east-north-up [enu]",
)
parser.add_option(
"--padding-factor",
type=float,
default=3.0,
dest="padding_factor",
help="extend time window on either side, in multiples of 1/<fmin_hz> "
"(default: 5)",
)
parser.add_option(
"--credentials",
dest="user_credentials",
action="append",
default=[],
metavar="SITE,USER,PASSWD",
help="user credentials for specific site to access restricted data "
"(this option can be repeated)",
)
parser.add_option(
"--token",
dest="auth_tokens",
metavar="SITE,FILENAME",
action="append",
default=[],
help="user authentication token for specific site to access "
"restricted data (this option can be repeated)",
)
parser.add_option(
"--sites",
dest="sites",
metavar="SITE1,SITE2,...",
# default='bgr',
default="http://ws.gpi.kit.edu,bgr,http://188.246.25.142:8080",
help='sites to query (available: %s, default: "%%default"' %
", ".join(g_sites_available),
)
parser.add_option(
"--band-codes",
dest="priority_band_code",
metavar="V,L,M,B,H,S,E,...",
default="V,L,M,B,H,E",
help="select and prioritize band codes (default: %default)",
)
parser.add_option(
"--instrument-codes",
dest="priority_instrument_code",
metavar="H,L,G,...",
default="H,L,O,",
help="select and prioritize instrument codes (default: %default)",
)
parser.add_option(
"--radius-min",
dest="radius_min",
metavar="VALUE",
default=0.0,
type=float,
help="minimum radius [km]",
)
parser.add_option(
"--tinc",
dest="tinc",
metavar="VALUE",
default=3600.0 * 12.0,
type=float,
help="length of seperate saved files in s",
)
parser.add_option(
"--nstations-wanted",
dest="nstations_wanted",
metavar="N",
type=int,
help="number of stations to select initially",
)
(options, args) = parser.parse_args(sys.argv[1:])
if len(args) not in (9, 6, 5):
parser.print_help()
sys.exit(1)
if options.debug:
util.setup_logging(program_name, "debug")
else:
util.setup_logging(program_name, "info")
if options.local_responses_pz and options.local_responses_resp:
logger.critical("cannot use local responses in PZ and RESP "
"format at the same time")
sys.exit(1)
n_resp_opt = 0
for resp_opt in (
options.local_responses_pz,
options.local_responses_resp,
options.local_responses_stationxml,
):
if resp_opt:
n_resp_opt += 1
if n_resp_opt > 1:
logger.critical("can only handle local responses from either PZ or "
"RESP or StationXML. Cannot yet merge different "
"response formats.")
sys.exit(1)
if options.local_responses_resp and not options.local_stations:
logger.critical("--local-responses-resp can only be used "
"when --stations is also given.")
sys.exit(1)
try:
ename = ""
magnitude = None
mt = None
if len(args) == 9:
time = util.str_to_time(args[0] + " " + args[1])
lat = float(args[2])
lon = float(args[3])
depth = float(args[4]) * km
iarg = 5
elif len(args) == 6:
if args[1].find(":") == -1:
sname_or_date = None
lat = float(args[0])
lon = float(args[1])
event = None
time = None
else:
sname_or_date = args[0] + " " + args[1]
iarg = 2
elif len(args) == 5:
sname_or_date = args[0]
iarg = 1
if len(args) in (6, 5) and sname_or_date is not None:
events = get_events_by_name_or_date([sname_or_date],
catalog=geofon)
if len(events) == 0:
logger.critical("no event found")
sys.exit(1)
elif len(events) > 1:
logger.critical("more than one event found")
sys.exit(1)
event = events[0]
time = event.time
lat = event.lat
lon = event.lon
depth = event.depth
ename = event.name
magnitude = event.magnitude
mt = event.moment_tensor
radius = float(args[iarg]) * km
fmin = float(args[iarg + 1])
sample_rate = float(args[iarg + 2])
eventname = args[iarg + 3]
event_dir = op.join("data", "events", eventname)
output_dir = op.join(event_dir, "waveforms")
except:
raise
parser.print_help()
sys.exit(1)
if options.force and op.isdir(event_dir):
if not options.continue_:
shutil.rmtree(event_dir)
if op.exists(event_dir) and not options.continue_:
logger.critical(
'directory "%s" exists. Delete it first or use the --force option'
% event_dir)
sys.exit(1)
util.ensuredir(output_dir)
if time is not None:
event = model.Event(
time=time,
lat=lat,
lon=lon,
depth=depth,
name=ename,
magnitude=magnitude,
moment_tensor=mt,
)
if options.window == "full":
if event is None:
logger.critical("need event for --window=full")
sys.exit(1)
low_velocity = 1500.0
timewindow = VelocityWindow(low_velocity,
tpad=options.padding_factor / fmin)
tmin, tmax = timewindow(time, radius, depth)
elif options.window == "p":
if event is None:
logger.critical("need event for --window=p")
sys.exit(1)
phases = list(map(cake.PhaseDef, "P p".split()))
emod = cake.load_model()
tpad = options.padding_factor / fmin
timewindow = PhaseWindow(emod, phases, -tpad, tpad)
arrivaltimes = []
for dist in num.linspace(0, radius, 20):
try:
arrivaltimes.extend(timewindow(time, dist, depth))
except NoArrival:
pass
if not arrivaltimes:
logger.error("required phase arrival not found")
sys.exit(1)
tmin = min(arrivaltimes)
tmax = max(arrivaltimes)
else:
try:
stmin, stmax = options.window.split(",")
tmin = util.str_to_time(stmin.strip())
tmax = util.str_to_time(stmax.strip())
timewindow = FixedWindow(tmin, tmax)
except ValueError:
logger.critical('invalid argument to --window: "%s"' %
options.window)
sys.exit(1)
if event is not None:
event.name = eventname
tlen = tmax - tmin
tfade = tfade_factor / fmin
tpad = tfade
tmin -= tpad
tmax += tpad
priority_band_code = options.priority_band_code.split(",")
for s in priority_band_code:
if len(s) != 1:
logger.critical("invalid band code: %s" % s)
priority_instrument_code = options.priority_instrument_code.split(",")
for s in priority_instrument_code:
if len(s) != 1:
logger.critical("invalid instrument code: %s" % s)
station_query_conf = dict(
latitude=lat,
longitude=lon,
minradius=options.radius_min * km * cake.m2d,
maxradius=radius * cake.m2d,
channel=",".join("?%s?" % s for s in priority_band_code),
)
target_sample_rate = sample_rate
fmax = target_sample_rate
# target_sample_rate = None
# priority_band_code = ['H', 'B', 'M', 'L', 'V', 'E', 'S']
priority_units = ["M/S", "M", "M/S**2"]
output_units = "M"
sites = [x.strip() for x in options.sites.split(",") if x.strip()]
tinc = options.tinc
# for site in sites:
# if site not in g_sites_available:
# logger.critical('unknown FDSN site: %s' % site)
# sys.exit(1)
for s in options.user_credentials:
try:
site, user, passwd = s.split(",")
g_user_credentials[site] = user, passwd
except ValueError:
logger.critical('invalid format for user credentials: "%s"' % s)
sys.exit(1)
for s in options.auth_tokens:
try:
site, token_filename = s.split(",")
with open(token_filename, "r") as f:
g_auth_tokens[site] = f.read()
except (ValueError, OSError, IOError):
logger.critical("cannot get token from file: %s" % token_filename)
sys.exit(1)
fn_template0 = (
"data_%(network)s.%(station)s.%(location)s.%(channel)s_%(tmin)s.mseed")
fn_template_raw = op.join(output_dir, "raw", fn_template0)
fn_template_raw_folder = op.join(output_dir, "raw/", "traces.mseed")
fn_stations_raw = op.join(output_dir, "stations.raw.txt")
fn_template_rest = op.join(output_dir, "rest", fn_template0)
fn_commandline = op.join(output_dir, "seigerdown.command")
ftap = (ffade_factors[0] * fmin, fmin, fmax, ffade_factors[1] * fmax)
# chapter 1: download
sxs = []
for site in sites:
try:
extra_args = {
"iris": dict(matchtimeseries=True),
}.get(site, {})
extra_args.update(station_query_conf)
if site == "geonet":
extra_args.update(starttime=tmin, endtime=tmax)
else:
extra_args.update(
startbefore=tmax,
endafter=tmin,
includerestricted=(site in g_user_credentials
or site in g_auth_tokens),
)
logger.info("downloading channel information (%s)" % site)
sx = fdsn.station(site=site,
format="text",
level="channel",
**extra_args)
except fdsn.EmptyResult:
logger.error("No stations matching given criteria. (%s)" % site)
sx = None
if sx is not None:
sxs.append(sx)
if all(sx is None for sx in sxs) and not options.local_data:
sys.exit(1)
nsl_to_sites = defaultdict(list)
nsl_to_station = {}
for sx, site in zip(sxs, sites):
site_stations = sx.get_pyrocko_stations()
for s in site_stations:
nsl = s.nsl()
nsl_to_sites[nsl].append(site)
if nsl not in nsl_to_station:
nsl_to_station[nsl] = s # using first site with this station
logger.info("number of stations found: %i" % len(nsl_to_station))
# station weeding
nsls_selected = None
if options.nstations_wanted:
stations_all = [
nsl_to_station[nsl_] for nsl_ in sorted(nsl_to_station.keys())
]
for s in stations_all:
s.set_event_relative_data(event)
stations_selected = weeding.weed_stations(stations_all,
options.nstations_wanted)[0]
nsls_selected = set(s.nsl() for s in stations_selected)
logger.info("number of stations selected: %i" % len(nsls_selected))
have_data = set()
if options.continue_:
fns = glob.glob(fn_template_raw % starfill())
p = pile.make_pile(fns)
else:
fns = []
have_data_site = {}
could_have_data_site = {}
for site in sites:
have_data_site[site] = set()
could_have_data_site[site] = set()
available_through = defaultdict(set)
it = 0
nt = int(math.ceil((tmax - tmin) / tinc))
for it in range(nt):
tmin_win = tmin + it * tinc
tmax_win = min(tmin + (it + 1) * tinc, tmax)
logger.info("time window %i/%i (%s - %s)" %
(it + 1, nt, util.tts(tmin_win), util.tts(tmax_win)))
have_data_this_window = set()
if options.continue_:
trs_avail = p.all(tmin=tmin_win, tmax=tmax_win, load_data=False)
for tr in trs_avail:
have_data_this_window.add(tr.nslc_id)
for site, sx in zip(sites, sxs):
if sx is None:
continue
selection = []
channels = sx.choose_channels(
target_sample_rate=target_sample_rate,
priority_band_code=priority_band_code,
priority_units=priority_units,
priority_instrument_code=priority_instrument_code,
timespan=(tmin_win, tmax_win),
)
for nslc in sorted(channels.keys()):
if nsls_selected is not None and nslc[:3] not in nsls_selected:
continue
could_have_data_site[site].add(nslc)
if nslc not in have_data_this_window:
channel = channels[nslc]
if event:
lat_, lon_ = event.lat, event.lon
else:
lat_, lon_ = lat, lon
dist = orthodrome.distance_accurate50m_numpy(
lat_, lon_, channel.latitude.value,
channel.longitude.value)
if event:
depth_ = event.depth
time_ = event.time
else:
depth_ = None
time_ = None
tmin_, tmax_ = timewindow(time_, dist, depth_)
tmin_this = tmin_ - tpad
tmax_this = tmax_ + tpad
tmin_req = max(tmin_win, tmin_this)
tmax_req = min(tmax_win, tmax_this)
if channel.sample_rate:
deltat = 1.0 / channel.sample_rate.value
else:
deltat = 1.0
if tmin_req < tmax_req:
# extend time window by some samples because otherwise
# sometimes gaps are produced
selection.append(nslc + (tmin_req - deltat * 10.0,
tmax_req + deltat * 10.0))
if options.dry_run:
for (net, sta, loc, cha, tmin, tmax) in selection:
available_through[net, sta, loc, cha].add(site)
else:
neach = 100
i = 0
nbatches = ((len(selection) - 1) // neach) + 1
while i < len(selection):
selection_now = selection[i:i + neach]
f = tempfile.NamedTemporaryFile()
try:
sbatch = ""
if nbatches > 1:
sbatch = " (batch %i/%i)" % (
(i // neach) + 1, nbatches)
logger.info("downloading data (%s)%s" % (site, sbatch))
data = fdsn.dataselect(site=site,
selection=selection_now,
**get_user_credentials(site))
while True:
buf = data.read(1024)
if not buf:
break
f.write(buf)
f.flush()
trs = io.load(f.name)
for tr in trs:
if tr.station == "7869":
tr.station = "MOER"
tr.network = "LE"
tr.location = ""
try:
tr.chop(tmin_win, tmax_win)
have_data.add(tr.nslc_id)
have_data_site[site].add(tr.nslc_id)
except trace.NoData:
pass
fns2 = io.save(trs, fn_template_raw)
io.save(trs, fn_template_raw_folder)
for fn in fns2:
if fn in fns:
logger.warn("overwriting file %s", fn)
fns.extend(fns2)
except fdsn.EmptyResult:
pass
except HTTPError:
logger.warn("an error occurred while downloading data "
"for channels \n %s" %
"\n ".join(".".join(x[:4])
for x in selection_now))
f.close()
i += neach
if options.dry_run:
nslcs = sorted(available_through.keys())
all_channels = defaultdict(set)
all_stations = defaultdict(set)
def plural_s(x):
return "" if x == 1 else "s"
for nslc in nslcs:
sites = tuple(sorted(available_through[nslc]))
logger.info("selected: %s.%s.%s.%s from site%s %s" %
(nslc + (plural_s(len(sites)), "+".join(sites))))
all_channels[sites].add(nslc)
all_stations[sites].add(nslc[:3])
nchannels_all = 0
nstations_all = 0
for sites in sorted(all_channels.keys(),
key=lambda sites: (-len(sites), sites)):
nchannels = len(all_channels[sites])
nstations = len(all_stations[sites])
nchannels_all += nchannels
nstations_all += nstations
logger.info("selected (%s): %i channel%s (%i station%s)" % (
"+".join(sites),
nchannels,
plural_s(nchannels),
nstations,
plural_s(nstations),
))
logger.info("selected total: %i channel%s (%i station%s)" % (
nchannels_all,
plural_s(nchannels_all),
nstations_all,
plural_s(nstations_all),
))
logger.info("dry run done.")
sys.exit(0)
for nslc in have_data:
# if we are in continue mode, we have to guess where the data came from
if not any(nslc in have_data_site[site] for site in sites):
for site in sites:
if nslc in could_have_data_site[site]:
have_data_site[site].add(nslc)
sxs = {}
for site in sites:
selection = []
for nslc in sorted(have_data_site[site]):
selection.append(nslc + (tmin - tpad, tmax + tpad))
if selection:
logger.info("downloading response information (%s)" % site)
sxs[site] = fdsn.station(site=site,
level="response",
selection=selection)
sited = site
if site == "http://192.168.11.220:8080":
sited = "bgr_internal"
elif site == "http://ws.gpi.kit.edu":
sited = "kit"
if site == "http://188.246.25.142:8080":
sited = "moer"
sxs[site].dump_xml(filename=op.join(output_dir, "stations.%s.xml" %
sited))
# chapter 1.5: inject local data
if options.local_data:
have_data_site["local"] = set()
plocal = pile.make_pile(options.local_data, fileformat="detect")
for traces in plocal.chopper_grouped(gather=lambda tr: tr.nslc_id,
tmin=tmin,
tmax=tmax,
tinc=tinc):
for tr in traces:
if tr.station == "7869":
tr.station = "MOER"
tr.network = "LE"
tr.location = ""
if tr.nslc_id not in have_data:
fns.extend(io.save(traces, fn_template_raw))
have_data_site["local"].add(tr.nslc_id)
have_data.add(tr.nslc_id)
sites.append("local")
if options.local_responses_pz:
sxs["local"] = epz.make_stationxml(
epz.iload(options.local_responses_pz))
if options.local_responses_resp:
local_stations = []
for fn in options.local_stations:
local_stations.extend(model.load_stations(fn))
sxs["local"] = resp.make_stationxml(
local_stations, resp.iload(options.local_responses_resp))
if options.local_responses_stationxml:
sxs["local"] = stationxml.load_xml(
filename=options.local_responses_stationxml)
# chapter 1.6: dump raw data stations file
nsl_to_station = {}
for site in sites:
if site in sxs:
stations = sxs[site].get_pyrocko_stations(timespan=(tmin, tmax))
for s in stations:
nsl = s.nsl()
if nsl not in nsl_to_station:
nsl_to_station[nsl] = s
stations = [nsl_to_station[nsl_] for nsl_ in sorted(nsl_to_station.keys())]
util.ensuredirs(fn_stations_raw)
model.dump_stations(stations, fn_stations_raw)
dump_commandline(sys.argv, fn_commandline)
# chapter 2: restitution
if not fns:
logger.error("no data available")
sys.exit(1)
p = pile.make_pile(fns, show_progress=False)
p.get_deltatmin()
otinc = None
if otinc is None:
otinc = nice_seconds_floor(p.get_deltatmin() * 500000.0)
otinc = 3600.0
otmin = math.floor(p.tmin / otinc) * otinc
otmax = math.ceil(p.tmax / otinc) * otinc
otpad = tpad * 2
fns = []
rest_traces_b = []
win_b = None
for traces_a in p.chopper_grouped(gather=lambda tr: tr.nslc_id,
tmin=otmin,
tmax=otmax,
tinc=otinc,
tpad=otpad):
rest_traces_a = []
win_a = None
for tr in traces_a:
if tr.station == "7869":
tr.station = "MOER"
tr.network = "LE"
tr.location = ""
win_a = tr.wmin, tr.wmax
if win_b and win_b[0] >= win_a[0]:
fns.extend(cut_n_dump(rest_traces_b, win_b, fn_template_rest))
rest_traces_b = []
win_b = None
response = None
failure = []
for site in sites:
try:
if site not in sxs:
continue
response = sxs[site].get_pyrocko_response(
tr.nslc_id,
timespan=(tr.tmin, tr.tmax),
fake_input_units=output_units,
)
break
except stationxml.NoResponseInformation:
failure.append("%s: no response information" % site)
except stationxml.MultipleResponseInformation:
failure.append("%s: multiple response information" % site)
if response is None:
failure = ", ".join(failure)
else:
failure = ""
try:
rest_tr = tr.transfer(tfade, ftap, response, invert=True)
rest_traces_a.append(rest_tr)
except (trace.TraceTooShort, trace.NoData):
failure = "trace too short"
if failure:
logger.warn("failed to restitute trace %s.%s.%s.%s (%s)" %
(tr.nslc_id + (failure, )))
if rest_traces_b:
rest_traces = trace.degapper(rest_traces_b + rest_traces_a,
deoverlap="crossfade_cos")
fns.extend(cut_n_dump(rest_traces, win_b, fn_template_rest))
rest_traces_a = []
if win_a:
for tr in rest_traces:
if tr.station == "7869":
tr.station = "MOER"
tr.network = "LE"
tr.location = ""
try:
rest_traces_a.append(
tr.chop(win_a[0], win_a[1] + otpad, inplace=False))
except trace.NoData:
pass
rest_traces_b = rest_traces_a
win_b = win_a
fns.extend(cut_n_dump(rest_traces_b, win_b, fn_template_rest))
# chapter 3: rotated restituted traces for inspection
if not event:
sys.exit(0)
fn_template1 = "DISPL.%(network)s.%(station)s.%(location)s.%(channel)s"
fn_waveforms = op.join(output_dir, "prepared", fn_template1)
fn_stations = op.join(output_dir, "stations.prepared.txt")
fn_event = op.join(event_dir, "event.txt")
nsl_to_station = {}
for site in sites:
if site in sxs:
stations = sxs[site].get_pyrocko_stations(timespan=(tmin, tmax))
for s in stations:
nsl = s.nsl()
if nsl not in nsl_to_station:
nsl_to_station[nsl] = s
p = pile.make_pile(fns, show_progress=False)
deltat = None
if sample_rate is not None:
deltat = 1.0 / sample_rate
used_stations = []
for nsl, s in nsl_to_station.items():
s.set_event_relative_data(event)
traces = p.all(trace_selector=lambda tr: tr.nslc_id[:3] == nsl)
keep = []
for tr in traces:
if deltat is not None:
try:
tr.downsample_to(deltat, snap=True, allow_upsample_max=5)
keep.append(tr)
except util.UnavailableDecimation as e:
logger.warn("Cannot downsample %s.%s.%s.%s: %s" %
(tr.nslc_id + (e, )))
continue
if options.out_components == "rtu":
pios = s.guess_projections_to_rtu(out_channels=("R", "T", "Z"))
elif options.out_components == "enu":
pios = s.guess_projections_to_enu(out_channels=("E", "N", "Z"))
else:
assert False
for (proj, in_channels, out_channels) in pios:
proc = trace.project(traces, proj, in_channels, out_channels)
for tr in proc:
for ch in out_channels:
if ch.name == tr.channel:
s.add_channel(ch)
if proc:
io.save(proc, fn_waveforms)
used_stations.append(s)
stations = list(used_stations)
util.ensuredirs(fn_stations)
model.dump_stations(stations, fn_stations)
model.dump_events([event], fn_event)
logger.info("prepared waveforms from %i stations" % len(stations))
def collectSemb(SembList, Config, Origin, Folder, ntimes, arrays, switch,
array_centers):
'''
method to collect semblance matrizes from all processes and write them to file for each timestep
'''
Logfile.add('start collect in collectSemb')
cfg = ConfigObj(dict=Config)
origin = ConfigObj(dict=Origin)
dimX = cfg.dimX() #('dimx')
dimY = cfg.dimY() #('dimy')
if switch == 0:
winlen = cfg.winlen() #('winlen')
step = cfg.step() #('step')
if switch == 1:
winlen = cfg.winlen_f2() #('winlen')
step = cfg.step_f2() #('step')
latv = []
lonv = []
gridspacing = cfg.Float('gridspacing')
migpoints = dimX * dimY
o_lat = origin.lat() # float(Origin['lat'])
o_lon = origin.lon() # float(Origin['lon'])
oLatul = 0
oLonul = 0
z = 0
for i in xrange(dimX):
oLatul = o_lat - ((dimX - 1) / 2) * gridspacing + i * gridspacing
if z == 0 and i == 0:
Latul = oLatul
o = 0
for j in xrange(dimY):
oLonul = o_lon - ((dimY - 1) / 2) * gridspacing + j * gridspacing
if o == 0 and j == 0:
Lonul = oLonul
latv.append(oLatul)
lonv.append(oLonul)
tmp = 1
origin = DataTypes.dictToLocation(Origin)
i = 0
#for a in SembList:
# tmp = num.zeros(num.shape(a))
azis = []
for a in SembList:
x = array_centers[i][0]
y = array_centers[i][1]
delta = orthodrome.distance_accurate50m_numpy(x, y, origin.lat,
origin.lon)
#a = a*((1./delta**2)*1.e+15)
tmp *= a
#azis.append(toAzimuth(float(Origin['lat']), float(Origin['lon']),x, y))
i = i + 1
#min_coor = num.zeros([i,2])
#i = 0
#for a in SembList:
# deltas = []
# x = array_centers[i][0]
# y = array_centers[i][1]
# for k in range(0,len(latv)):
# delta = orthodrome.distance_accurate50m_numpy(x, y, latv[k], lonv[k])
# deltas.append(orthodrome.distance_accurate50m_numpy(x, y, latv[k], lonv[k]))
# if delta <= num.min(deltas):
# min_coor[i]= [latv[k], lonv[k]]
# i = i+1
# array_overlap = num.average(min_coor, axis=0)
# delta_center = orthodrome.distance_accurate50m_numpy(array_overlap[0], array_overlap[1], origin.lat, origin.lon)
# print(array_overlap)
# print(delta_center)
# diff_center_lat = origin.lat-array_overlap[0]
# diff_center_lon = origin.lon-array_overlap[1]
# print(diff_center_lat)
# print(diff_center_lon)
#for a in SembList:
#if num.mean(a)>0:
# tmp *= a
sembmaxvaluev = num.ndarray(ntimes, dtype=float)
sembmaxlatv = num.ndarray(ntimes, dtype=float)
sembmaxlonv = num.ndarray(ntimes, dtype=float)
rc = UTCDateTime(Origin['time'])
rcs = '%s-%s-%s_%02d:%02d:%02d' % (rc.day, rc.month, rc.year, rc.hour,
rc.minute, rc.second)
d = rc.timestamp
usedarrays = arrays
folder = Folder['semb']
fobjsembmax = open(os.path.join(folder, 'sembmax_%s.txt' % (switch)), 'w')
norm = num.max(num.max(tmp, axis=1))
max_p = 0.
sum_i = 0.
for a, i in enumerate(tmp):
if a < 1:
sum_i *= i
for a, i in enumerate(tmp):
if a < 1:
max = num.max(sum_i[:])
for j in range(migpoints):
if i[j] > num.max(i[:]) * 0.9 and i[j] > max_p:
latvmax = latv[j]
lonvmax = lonv[j]
max_p = i[j]
# delta_lat = origin.lat-latvmax
# delta_lon = origin.lon-lonvmax
#for a, i in enumerate(tmp):
# max_pos = [l for l, k in enumerate(i) if k == i.max()][0]
# delta_lat = origin.lat-latv[max_pos]
# delta_lon = origin.lon-lonv[max_pos]
for j in range(migpoints):
latv[j] = latv[j] #+delta_lat
lonv[j] = lonv[j] #+delta_lon
# latv.append(latv[j]-delta_lat)
# lonv.append(lonv[j]-delta_lon)
#nix = []
#for a, i in enumerate(tmp):
# for j in range(migpoints):
# if i[j]/norm > num.max(sum_i/norm)*0.4:
# if j in nix:
# pass
# else:
# latv[j] = latv[j]+delta_lat
# lonv[j] = lonv[j]+delta_lon
# nix.append(j)
#if i[j]/norm > num.max(sum_i/norm)*0.4:
# print('yes')
# delta_lat = origin.lat-latv[j]
# delta_lon = origin.lon-lonv[j]
# print delta_lat, delta_lon, latvmax, lonvmax
# print latv[j], lonv[j], origin.lat, origin.lon
# ix = num.where(latv[j]+delta_lat)[0][0]
# iy = num.where(lonv[j]+delta_lon)[0][0]
# lat = latv[j].copy()
# lon = lonv[j].copy()
# latv[j] = latv[ix]
## lonv[j] = lonv[iy]
# lonv[iy]
# #latv[j] = latv[j]+delta_lat
#lonv[j] = lonv[j]+delta_lon
# print latv[j], lonv[j]
#
for a, i in enumerate(tmp):
logger.info('timestep %d' % a)
print(a)
fobj = open(
os.path.join(folder,
'%s-%s_%03d.ASC' % (switch, Origin['depth'], a)), 'w')
fobj.write('# %s , %s\n' % (d, rcs))
fobj.write('# step %ds| ntimes %d| winlen: %ds\n' %
(step, ntimes, winlen))
fobj.write('# \n')
fobj.write('# southwestlat: %.2f dlat: %f nlat: %f \n' %
(Latul, gridspacing, dimX))
fobj.write('# southwestlon: %.2f dlon: %f nlon: %f \n' %
(Lonul, gridspacing, dimY))
fobj.write('# ddepth: 0 ndepth: 1 \n')
sembmax = 0
sembmaxX = 0
sembmaxY = 0
uncert = num.std(i) #maybe not std?
for j in range(migpoints):
x = latv[j] #+delta_lat
y = lonv[j] #+delta_lon
# if i[j]/norm > num.max(i[:]/norm)*0.1:
# delta_lat = origin.lat-latv[max_pos]
# delta_lon = origin.lon-lonv[max_pos]
# print delta_lat, delta_lon, latv[max_pos], lonv[max_pos]
# print latv[j], lonv[j], origin.lat, origin.lon
# x = latv[j]+delta_lat
# y = lonv[j]+delta_lon
# print x, y
semb = i[j] / norm
fobj.write('%.2f %.2f %.20f\n' % (x, y, semb))
# xd= latv[j]-delta_lat
# yd= lonv[j]-delta_lon
# sembd = 0.
# fobj.write('%.2f %.2f %.20f\n' %(xd,yd,sembd))
if semb > sembmax:
sembmax = semb
# search for maximum and position of maximum on semblance grid for given time step
sembmaxX = x
sembmaxY = y
delta = orthodrome.distance_accurate50m_numpy(x, y, origin.lat,
origin.lon)
azi = toAzimuth(float(Origin['lat']), float(Origin['lon']),
float(sembmaxX), float(sembmaxY))
sembmaxvaluev[a] = sembmax
sembmaxlatv[a] = sembmaxX
sembmaxlonv[a] = sembmaxY
fobjsembmax.write('%d %.3f %.3f %.30f %.30f %d %03f %f %03f\n' %
(a * step, sembmaxX, sembmaxY, sembmax, uncert,
usedarrays, delta, float(azi), delta * 119.19))
fobj.close()
fobjsembmax.close()
trigger.writeSembMaxValue(sembmaxvaluev, sembmaxlatv, sembmaxlonv, ntimes,
Config, Folder)
inspect_semb = cfg.Bool('inspect_semb')
if inspect_semb is True:
trigger.semblancestalta(sembmaxvaluev, sembmaxlatv, sembmaxlonv)
return sembmaxvaluev
def call(self):
try:
global vtk
import vtk
from vtk.util import numpy_support
import sys
sys.path[0:0] = [self.module_dir()]
from grid_topo import setup_vtk_map_actor
sys.path[0:1] = []
except ImportError as _import_error:
self.fail('\nImportError:\n%s' % _import_error)
vtk = None
self.cleanup()
stations = []
events = []
cone_actors = []
sphere_actors = []
if self.want_stations:
stations = self.get_stations()
if self.want_events:
markers = self.get_selected_event_markers()
if len(markers) == 0:
tmin, tmax = self.get_selected_time_range(fallback=True)
markers = filter(lambda x: tmin < x.tmin < tmax,
self.get_event_markers())
events = [m.get_event() for m in markers]
all_lats = []
all_lons = []
for s in stations:
all_lats.append(s.lat)
all_lons.append(s.lon)
for e in events:
all_lats.append(e.lat)
all_lons.append(e.lon)
center_lat, center_lon = ortho.geographic_midpoint(
num.array(all_lats), num.array(all_lons))
center_lats = num.array([center_lat]*len(all_lats))
center_lons = num.array([center_lon]*len(all_lons))
distances = ortho.distance_accurate50m_numpy(
num.array(all_lats), num.array(all_lons),
center_lats, center_lons)
distance_max = num.max(distances)
size = distance_max / 50.
if len(events) != 0:
ns, es, depths = self.locations_to_ned(events)
times = [e.time for e in events]
adata = num.array((es, ns, -depths))
adata = adata.flatten(order='F')
data = numpy_support.numpy_to_vtk(
adata, deep=True, array_type=vtk.VTK_FLOAT)
data.SetNumberOfComponents(3)
sphere_actors = self.events_to_vtksphere_actors(data, times, size=size/2.)
if len(stations) != 0:
ns, es, depths = self.locations_to_ned(stations,
has_elevation=True)
adata = num.array((es, ns, -depths)).flatten(order='F')
data = numpy_support.numpy_to_vtk(
adata, deep=True, array_type=vtk.VTK_FLOAT)
data.SetNumberOfComponents(3)
cone_actors = self.stations_to_vtkcone_actors(data, size=size)
if self.want_topo:
distance_max += self.margin_radius * 1000
self.topo_actor = setup_vtk_map_actor(
center_lat, center_lon, distance_max,
super_elevation=self.z_scale,
decimation=int(self.z_decimation or 1),
smoothing=self.smoothing)
self.frame = self.vtk_frame()
for actor in cone_actors:
actor.GetProperty().SetColor(0., 0., 1.)
self.frame.add_actor(actor)
for actor in sphere_actors:
self.frame.add_actor(actor)
if self.topo_actor:
self.frame.add_actor(self.topo_actor)
self.frame.renderer.SetBackground(0.01, 0.05, 0.1)
self.frame.init()
derec_home = os.environ['DEREC_HOME']
store_id = 'doctar_mainland_20Hz'
e = LocalEngine(store_superdirs=[pjoin(derec_home, 'fomostos')])
store = e.get_store(store_id)
m = store.config.earthmodel_1d
event = model.Event(load=pjoin(derec_home, 'mseeds/doctar/doctar_2011-11-01/doctar_2011-11-01_quakefile.dat'))
statfn = pjoin(derec_home, 'mseeds/doctar/doctar_2011-11-01/stations.txt')
stations = model.load_stations(statfn)
stations_distance = collections.defaultdict()
for s in stations:
d = orthodrome.distance_accurate50m_numpy(s.lat,s.lon,event.lat, event.lon)
stations_distance[d] = s.station
#phases = ['p', 'P', 'Pv11.p']
phases = ['p']
colormap = collections.defaultdict()
num_colors = 12
colors = list(num.linspace(0,254,num_colors))
cmap = plt.get_cmap('hsv')
distances = num.linspace(1, 58, 120)*km*cake.m2d
#distances = num.linspace(1, 58, 40)*km*cake.m2d
t_diff = collections.defaultdict(list)
t_diff_d = collections.defaultdict(list)
toplot = []
def filter_oob(sources, targets, config):
'''Filter sources that will be out of bounds of GF database.'''
nsources, ntargets = len(sources), len(targets)
slats, slons = num.empty(nsources), num.empty(nsources)
sdepth = num.empty(nsources)
tlats, tlons = num.empty(ntargets), num.empty(ntargets)
televations = num.empty(ntargets)
for i_s, s in enumerate(sources):
slats[i_s], slons[i_s], sdepth[i_s] = *s.effective_latlon, s.depth
for i_t, t in enumerate(targets):
tlats[i_t], tlons[i_t] = t.effective_latlon
televations[i_t] = t.elevation
# dmax = config.distance_max
# nsources = len(sources)
# sources_out = []
# for i_s, s in enumerate(sources[::-1]):
# for t in targets:
# print(t.distance_to(s))
# if t.distance_to(s) > dmax:
# print('break')
# break
# else:
# print('fine')
# sources_out.append(s)
# return sources_out
dists = num.empty((ntargets, nsources))
depths = num.empty((ntargets, nsources))
for i in range(ntargets):
dists[i] = orthodrome.distance_accurate50m_numpy(
slats, slons, tlats[i], tlons[i])
depths[i] = sdepth + televations[i]
i_dist = num.where(
num.any(num.logical_or(dists > config.distance_max - 100,
dists < config.distance_min + 100),
axis=0))[0]
i_depth = num.where(
num.any(num.logical_or(depths > config.source_depth_max - 100,
depths < config.source_depth_min + 100),
axis=0))[0]
print(i_depth)
i_filter = num.union1d(i_depth, i_dist)
print(dists)
logger.warn('Removing %i / %i sources which would be out of bounds' %
(len(i_filter), nsources))
for i in i_filter[::-1]:
# print('.'*10)
# for t in targets:
# print(sources[i].distance_to(t))
sources.pop(i)
# del sources[i]
_d = [t.distance_to(s) for t in targets for s in sources]
print(max(_d))
print(min(_d))
_d = [s.depth + t.elevation for t in targets for s in sources]
print(min(_d))
print(max(_d))
return sources
def testDistanceArrayC(self):
ntest = 10000
locs = self.get_critical_random_locations(ntest)
orthodrome.distance_accurate50m_numpy(*locs, implementation='c')
def prep_orient(datapath,
st,
loc,
catalog,
dir_ro,
v_rayleigh,
bp,
dt_start,
dt_stop,
ccmin=0.80,
plot_heatmap=False,
plot_distr=False,
debug=False):
"""
Perform orientation analysis using Rayleigh waves, main function.
time wdw: 20s before 4.0km/s arrival and 600 s afterwards
(Stachnik et al. 2012)
- compute radial component for back values of 0 to 360 deg
- for each c-c of hilbert(R) with Z comp.
- call plotting functions and/or write results to file
:param datapath: path to rrd data
:param st: current station (pyrocko station object)
:param catalog: list of pyrocko events used for analysis
:param dir_ro: output directory
:param plot_heatmap: bool, optional
:param plot_distr: bool, optional
"""
logs = logging.getLogger('prep_orient')
st_data_pile = pile.make_pile(datapath,
regex='%s_%s_' % (st.network, st.station),
show_progress=False)
n_ev = len(catalog)
if st_data_pile.tmin is not None and st_data_pile.tmax is not None:
# calculate dist between all events and current station
r_arr_by_ev = num.empty(n_ev)
ev_lats = num.asarray([ev.lat for ev in catalog])
ev_lons = num.asarray([ev.lon for ev in catalog])
dists = distance_accurate50m_numpy(a_lats=ev_lats,
a_lons=ev_lons,
b_lats=st.lat,
b_lons=st.lon,
implementation='c')
r_arr_by_ev = (dists / 1000.) / v_rayleigh
cc_i_ev_vs_rota = num.empty((n_ev, 360))
rot_angles = range(-180, 180, 1)
for i_ev, ev in enumerate(catalog):
arrT = ev.time + r_arr_by_ev[i_ev]
start_twd1 = ev.time
end_twd1 = arrT + 1800
trZ = get_tr_by_cha(st_data_pile, start_twd1, end_twd1, loc, 'Z')
trR = get_tr_by_cha(st_data_pile, start_twd1, end_twd1, loc, 'R')
trT = get_tr_by_cha(st_data_pile, start_twd1, end_twd1, loc, 'T')
start_twd2 = ev.time + r_arr_by_ev[i_ev] - dt_start
end_twd2 = arrT + dt_stop
if len(trZ) == 1 and len(trR) == 1 and len(trT) == 1:
trZ = trZ[0]
trR = trR[0]
trT = trT[0]
# debugging - window selection:
if debug is True:
trace.snuffle([trZ, trR, trT],
markers=[
pm.Marker(nslc_ids=[
trZ.nslc_id, trR.nslc_id, trT.nslc_id
],
tmin=start_twd2,
tmax=end_twd2),
pm.Marker(nslc_ids=[
trZ.nslc_id, trR.nslc_id, trT.nslc_id
],
tmin=arrT,
tmax=arrT + 3)
])
else:
cc_i_ev_vs_rota[i_ev, :] = num.nan
continue
try:
trZ.bandpass(bp[0], bp[1], bp[2])
trZ.chop(tmin=start_twd2, tmax=end_twd2)
except trace.NoData:
logs.warning('no data %s %s %s' % (trZ, trR, trT))
continue
for i_r, r in enumerate(rot_angles):
print('rotation angle [deg]: %5d' % r, end='\r')
rot_2, rot_3 = trace.rotate(traces=[trR, trT],
azimuth=r,
in_channels=['R', 'T'],
out_channels=['2', '3'])
rot_2_y = rot_2.ydata
rot_2_hilb = num.imag(trace.hilbert(rot_2_y, len(rot_2_y)))
rot_2_hilb_tr = trace.Trace(deltat=rot_2.deltat,
ydata=rot_2_hilb,
tmin=rot_2.tmin)
# problem: rot_2 and rot_2_hilb look exactly the same!
# --> no phase shift. why? should be num.imag!!!
# trace.snuffle([rot_2, rot_2_hilb_tr])
rot_2_hilb_tr.bandpass(bp[0], bp[1], bp[2])
rot_2_hilb_tr.chop(tmin=start_twd2, tmax=end_twd2)
# if st.station == 'RORO' and r == 0:
# trace.snuffle([rot_2_hilb_tr, trZ])
# normalize traces
trZ.ydata /= abs(max(trZ.ydata))
rot_2_hilb_tr.ydata /= abs(max(rot_2_hilb_tr.ydata))
c = trace.correlate(trZ,
rot_2_hilb_tr,
mode='valid',
normalization='normal')
t, coef = c.max()
t2, coef2 = max_or_min(c)
'''
if st.station == 'MATE' and r == 0:
print(i_ev, ev.name, ev.depth)
print(r, t, coef, t2, coef2)
trace.snuffle([trZ, trR, rot_2_hilb_tr])
'''
cc_i_ev_vs_rota[i_ev, i_r] = coef
'''
if st.station == 'MATE':
for i_ev in range(n_ev):
print(num.argmax(cc_i_ev_vs_rota[i_ev,:]),
num.max(cc_i_ev_vs_rota[i_ev,:]))
'''
if plot_heatmap is True:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 2))
cax = ax.imshow(cc_i_ev_vs_rota,
interpolation='nearest',
vmin=-1.0,
vmax=1.0,
aspect='auto',
extent=[-180, 180, n_ev, 0],
cmap='binary')
ax.set_ylabel('i_ev')
ax.set_xlabel('Correction angle (deg)')
ax.set_title('%s %s' % (st.network, st.station))
cbar = fig.colorbar(cax,
ticks=[0, 0.5, 1.0],
orientation='horizontal',
fraction=0.05,
pad=0.5)
cbar.ax.set_xticklabels(['0', '0.5', '1.0'])
plt.tight_layout()
# plt.show(fig)
fig.savefig(
os.path.join(
dir_ro, '%s_%s_%s_rot_cc_heatmap.png' %
(st.network, st.station, loc)))
plt.close()
if plot_distr is True:
plot_ccdistr_each_event(cc_i_ev_vs_rota, catalog, rot_angles, st,
loc, dir_ro)
median_a, mean_a, std_a, switched, n_ev =\
get_m_angle_switched(cc_i_ev_vs_rota, catalog, st, ccmin)
dict_ev_angle = get_m_angle_all(cc_i_ev_vs_rota, catalog, st, ccmin)
return median_a, mean_a, std_a, switched, dict_ev_angle, n_ev
for nslc in sorted(channels.keys()):
if nsls_selected is not None and nslc[:3] not in nsls_selected:
continue
could_have_data_site[site].add(nslc)
if nslc not in have_data_this_window:
channel = channels[nslc]
if event:
lat_, lon_ = event.lat, event.lon
else:
lat_, lon_ = lat, lon
dist = orthodrome.distance_accurate50m_numpy(
lat_, lon_, channel.latitude.value,
channel.longitude.value)
if event:
depth_ = event.depth
time_ = event.time
else:
depth_ = None
time_ = None
tmin_, tmax_ = timewindow(time_, dist, depth_)
tmin_this = tmin_ - tpad
tmax_this = tmax_ + tpad
tmin_req = max(tmin_win, tmin_this)
def optparse(required=(),
optional=(),
args=sys.argv,
usage='%prog [options]',
descr=None):
want = required + optional
parser = OptionParser(prog='cake',
usage=usage,
description=descr.capitalize() + '.',
add_help_option=False,
formatter=util.BetterHelpFormatter())
parser.add_option('-h',
'--help',
action='help',
help='Show help message and exit.')
if 'phases' in want:
group = OptionGroup(
parser, 'Phases', '''
Seismic phase arrivals may be either specified as traditional phase names (e.g.
P, S, PP, PcP, ...) or in Cake's own syntax which is more powerful. Use the
--classic option, for traditional phase names. Use the --phase option if you
want to define phases in Cake's syntax.
''')
group.add_option(
'--phase',
'--phases',
dest='phases',
action="append",
default=[],
metavar='PHASE1,PHASE2,...',
help='''Comma separated list of seismic phases in Cake\'s syntax.
The definition of a seismic propagation path in Cake's phase syntax is a string
consisting of an alternating sequence of "legs" and "knees".
A "leg" represents seismic wave propagation without any conversions,
encountering only super-critical reflections. Legs are denoted by "P", "p",
"S", or "s". The capital letters are used when the take-off of the "leg" is
in downward direction, while the lower case letters indicate a take-off in
upward direction.
A "knee" is an interaction with an interface. It can be a mode conversion, a
reflection, or propagation as a headwave or diffracted wave.
* conversion is simply denoted as: "(INTERFACE)" or "DEPTH"
* upperside reflection: "v(INTERFACE)" or "vDEPTH"
* underside reflection: "^(INTERFACE)" or "^DEPTH"
* normal kind headwave or diffracted wave: "v_(INTERFACE)" or "v_DEPTH"
The interface may be given by name or by depth: INTERFACE is the name of an
interface defined in the model, DEPTH is the depth of an interface in [km] (the
interface closest to that depth is chosen). If two legs appear consecutively
without an explicit "knee", surface interaction is assumed.
The preferred standard interface names in cake are "conrad", "moho", "cmb"
(core-mantle boundary), and "cb" (inner core boundary).
The phase definition may end with a backslash "\\", to indicate that the ray
should arrive at the receiver from above instead of from below. It is possible
to restrict the maximum and minimum depth of a "leg" by appending
"<(INTERFACE)" or "<DEPTH" or ">(INTERFACE)" or ">DEPTH" after the leg
character, respectively.
When plotting rays or travel-time curves, the color can be set by appending
"{COLOR}" to the phase definition, where COLOR is the name of a color or an RGB
or RGBA color tuple in the format "R/G/B" or "R/G/B/A", respectively. The
values can be normalized to the range [0, 1] or to [0, 255]. The latter is only
assumed when any of the values given exceeds 1.0.
''')
group.add_option(
'--classic',
dest='classic_phases',
action='append',
default=[],
metavar='PHASE1,PHASE2,...',
help='''Comma separated list of seismic phases in classic
nomenclature. Run "cake list-phase-map" for a list of available
phase names. When plotting, color can be specified in the same way
as in --phases.''')
parser.add_option_group(group)
if 'model' in want:
group = OptionGroup(parser, 'Model')
group.add_option(
'--model',
dest='model_filename',
metavar='(NAME or FILENAME)',
help='Use builtin model named NAME or user model from file '
'FILENAME. By default, the "ak135-f-continental.m" model is '
'used. Run "cake list-models" for a list of builtin models.')
group.add_option(
'--format',
dest='model_format',
metavar='FORMAT',
choices=['nd', 'hyposat'],
default='nd',
help='Set model file format (available: nd, hyposat; default: '
'nd).')
group.add_option(
'--crust2loc',
dest='crust2loc',
metavar='LAT,LON',
help='Set model from CRUST2.0 profile at location (LAT,LON).')
group.add_option(
'--crust2profile',
dest='crust2profile',
metavar='KEY',
help='Set model from CRUST2.0 profile with given KEY.')
parser.add_option_group(group)
if any(x in want
for x in ('zstart', 'zstop', 'distances', 'sloc', 'rloc')):
group = OptionGroup(parser, 'Source-receiver geometry')
if 'zstart' in want:
group.add_option('--sdepth',
dest='sdepth',
type='float',
default=0.0,
metavar='FLOAT',
help='Source depth [km] (default: 0)')
if 'zstop' in want:
group.add_option('--rdepth',
dest='rdepth',
type='float',
default=0.0,
metavar='FLOAT',
help='Receiver depth [km] (default: 0)')
if 'distances' in want:
group.add_option('--distances',
dest='sdist',
metavar='DISTANCES',
help='Surface distances as "start:stop:n" or '
'"dist1,dist2,..." [km]')
group.add_option('--sloc',
dest='sloc',
metavar='LAT,LON',
help='Source location (LAT,LON).')
group.add_option('--rloc',
dest='rloc',
metavar='LAT,LON',
help='Receiver location (LAT,LON).')
parser.add_option_group(group)
if 'material' in want:
group = OptionGroup(
parser, 'Material',
'An isotropic elastic material may be specified by giving '
'a combination of some of the following options. ')
group.add_option('--vp',
dest='vp',
default=None,
type='float',
metavar='FLOAT',
help='P-wave velocity [km/s]')
group.add_option('--vs',
dest='vs',
default=None,
type='float',
metavar='FLOAT',
help='S-wave velocity [km/s]')
group.add_option('--rho',
dest='rho',
default=None,
type='float',
metavar='FLOAT',
help='density [g/cm**3]')
group.add_option('--qp',
dest='qp',
default=None,
type='float',
metavar='FLOAT',
help='P-wave attenuation Qp (default: 1456)')
group.add_option('--qs',
dest='qs',
default=None,
type='float',
metavar='FLOAT',
help='S-wave attenuation Qs (default: 600)')
group.add_option('--poisson',
dest='poisson',
default=None,
type='float',
metavar='FLOAT',
help='Poisson ratio')
group.add_option('--lambda',
dest='lame_lambda',
default=None,
type='float',
metavar='FLOAT',
help='Lame parameter lambda [GPa]')
group.add_option('--mu',
dest='lame_mu',
default=None,
type='float',
metavar='FLOAT',
help='Shear modulus [GPa]')
group.add_option('--qk',
dest='qk',
default=None,
type='float',
metavar='FLOAT',
help='Bulk attenuation Qk')
group.add_option('--qmu',
dest='qmu',
default=None,
type='float',
metavar='FLOAT',
help='Shear attenuation Qmu')
parser.add_option_group(group)
if any(x in want for x in ('vred', 'as_degrees', 'accuracy', 'slowness',
'interface', 'aspect', 'shade_model')):
group = OptionGroup(parser, 'General')
if 'vred' in want:
group.add_option('--vred',
dest='vred',
type='float',
metavar='FLOAT',
help='Velocity for time reduction in plot [km/s]')
if 'as_degrees' in want:
group.add_option(
'--degrees',
dest='as_degrees',
action='store_true',
default=False,
help='Distances are in [deg] instead of [km], velocities in '
'[deg/s] instead of [km/s], slownesses in [s/deg] '
'instead of [s/km].')
if 'accuracy' in want:
group.add_option('--accuracy',
dest='accuracy',
type='float',
metavar='MAXIMUM_RELATIVE_RMS',
default=0.002,
help='Set accuracy for model simplification.')
if 'slowness' in want:
group.add_option(
'--slowness',
dest='slowness',
type='float',
metavar='FLOAT',
default=0.0,
help='Select surface slowness [s/km] (default: 0)')
if 'interface' in want:
group.add_option('--interface',
dest='interface',
metavar='(NAME or DEPTH)',
help='Name or depth [km] of interface to select')
if 'aspect' in want:
group.add_option('--aspect',
dest='aspect',
type='float',
metavar='FLOAT',
help='Aspect ratio for plot')
if 'shade_model' in want:
group.add_option('--no-shade-model',
dest='shade_model',
action='store_false',
default=True,
help='Suppress shading of earth model layers')
parser.add_option_group(group)
if any(x in want for x in ('output_format', )):
group = OptionGroup(parser, 'Output')
if 'output_format' in want:
group.add_option(
'--output-format',
dest='output_format',
metavar='FORMAT',
default='textual',
choices=('textual', 'nd'),
help='Set model output format (available: textual, nd, '
'default: textual)')
parser.add_option_group(group)
if usage == 'cake help-options':
parser.print_help()
(options, args) = parser.parse_args(args)
if len(args) != 2:
parser.error(
'Cake arguments should look like "--option" or "--option=...".')
d = {}
as_degrees = False
if 'as_degrees' in want:
as_degrees = options.as_degrees
d['as_degrees'] = as_degrees
if 'accuracy' in want:
d['accuracy'] = options.accuracy
if 'output_format' in want:
d['output_format'] = options.output_format
if 'aspect' in want:
d['aspect'] = options.aspect
if 'shade_model' in want:
d['shade_model'] = options.shade_model
if 'phases' in want:
phases = []
phase_colors = {}
try:
for ss in options.phases:
for s in ss.split(','):
s = process_color(s, phase_colors)
phases.append(cake.PhaseDef(s))
for pp in options.classic_phases:
for p in pp.split(','):
p = process_color(p, phase_colors)
phases.extend(cake.PhaseDef.classic(p))
except (cake.PhaseDefParseError, cake.UnknownClassicPhase) as e:
parser.error(e)
if not phases and 'phases' in required:
s = process_color('P', phase_colors)
phases.append(cake.PhaseDef(s))
if phases:
d['phase_colors'] = phase_colors
d['phases'] = phases
if 'model' in want:
if options.model_filename:
d['model'] = cake.load_model(options.model_filename,
options.model_format)
if options.crust2loc or options.crust2profile:
if options.crust2loc:
try:
args = tuple(
[float(x) for x in options.crust2loc.split(',')])
except Exception:
parser.error('format for --crust2loc option is '
'"LATITUDE,LONGITUDE"')
elif options.crust2profile:
args = (options.crust2profile.upper(), )
else:
assert False
if 'model' in d:
d['model'] = d['model'].replaced_crust(args)
else:
from pyrocko import crust2x2
profile = crust2x2.get_profile(*args)
d['model'] = cake.LayeredModel.from_scanlines(
cake.from_crust2x2_profile(profile))
if 'vred' in want:
d['vred'] = options.vred
if d['vred'] is not None:
if not as_degrees:
d['vred'] *= r2d * cake.km / cake.earthradius
if 'distances' in want:
distances = None
if options.sdist:
if options.sdist.find(':') != -1:
ssn = options.sdist.split(':')
if len(ssn) != 3:
parser.error('format for distances is '
'"min_distance:max_distance:n_distances"')
distances = num.linspace(*map(float, ssn))
else:
distances = num.array(list(map(float,
options.sdist.split(','))),
dtype=num.float)
if not as_degrees:
distances *= r2d * cake.km / cake.earthradius
if options.sloc and options.rloc:
try:
slat, slon = tuple([float(x) for x in options.sloc.split(',')])
rlat, rlon = tuple([float(x) for x in options.rloc.split(',')])
except Exception:
parser.error('format for --sloc and --rloc options is '
'"LATITUDE,LONGITUDE"')
distance_sr = orthodrome.distance_accurate50m_numpy(
slat, slon, rlat, rlon)
distance_sr *= r2d / cake.earthradius
if distances is not None:
distances = num.concatenate((distances, [distance_sr]))
else:
distances = num.array([distance_sr], dtype=num.float)
if distances is not None:
d['distances'] = distances
else:
if 'distances' not in required:
d['distances'] = None
if 'slowness' in want:
d['slowness'] = options.slowness / cake.d2r
if not as_degrees:
d['slowness'] /= cake.km * cake.m2d
if 'interface' in want:
if options.interface:
try:
d['interface'] = float(options.interface) * cake.km
except ValueError:
d['interface'] = options.interface
else:
d['interface'] = None
if 'zstart' in want:
d['zstart'] = options.sdepth * cake.km
if 'zstop' in want:
d['zstop'] = options.rdepth * cake.km
if 'material' in want:
md = {}
userfactor = dict(vp=1000.,
vs=1000.,
rho=1000.,
qp=1.,
qs=1.,
qmu=1.,
qk=1.,
lame_lambda=1.0e9,
lame_mu=1.0e9,
poisson=1.)
for k in userfactor.keys():
if getattr(options, k) is not None:
md[k] = getattr(options, k) * userfactor[k]
if not (bool('lame_lambda' in md) == bool('lame_mu' in md)):
parser.error('lambda and mu must be specified both.')
if 'lame_lambda' in md and 'lame_mu' in md:
md['lame'] = md.pop('lame_lambda'), md.pop('lame_mu')
if md:
try:
d['material'] = cake.Material(**md)
except cake.InvalidArguments as e:
parser.error(str(e))
for k in list(d.keys()):
if k not in want:
del d[k]
for k in required:
if k not in d:
if k == 'model':
d['model'] = cake.load_model('ak135-f-continental.m')
elif k == 'distances':
d['distances'] = num.linspace(10*cake.km, 100*cake.km, 10) \
/ cake.earthradius * r2d
elif k == 'phases':
d['phases'] = list(map(cake.PhaseDef, 'Pp'))
else:
parser.error('missing %s' % k)
return Anon(d)
def update_distances_and_angles(self,
indices=None,
want_angles=False,
want_distances=False):
'''Calculate and update distances and kagan angles between events.
:param indices: list of lists of indices (optional)
Ideally, indices are consecutive for best performance.'''
want_angles = want_angles or \
not self.marker_table_view.isColumnHidden(
_column_mapping['Kagan Angle [deg]'])
want_distances = want_distances or \
not self.marker_table_view.isColumnHidden(
_column_mapping['Dist [km]'])
if not (want_distances or want_angles):
return
indices = indices or [[]]
indices = [i for ii in indices for i in ii]
if len(indices) != 1:
return
if self.last_active_event == self.pile_viewer.get_active_event():
return
else:
self.last_active_event = self.pile_viewer.get_active_event()
markers = self.pile_viewer.markers
nmarkers = len(markers)
omarker = markers[indices[0]]
if not isinstance(omarker, EventMarker):
return
else:
oevent = omarker.get_event()
emarkers = [m for m in markers if isinstance(m, EventMarker)]
if len(emarkers) < 2:
return
else:
events = [em.get_event() for em in emarkers]
nevents = len(events)
if want_distances:
lats = num.zeros(nevents)
lons = num.zeros(nevents)
for i in range(nevents):
lats[i] = events[i].lat
lons[i] = events[i].lon
olats = num.zeros(nevents)
olons = num.zeros(nevents)
olats[:] = oevent.lat
olons[:] = oevent.lon
dists = orthodrome.distance_accurate50m_numpy(
lats, lons, olats, olons)
dists /= 1000.
dists = [round(x, 1) for x in dists]
self.distances = dict(list(zip(emarkers, dists)))
if want_angles:
if oevent.moment_tensor:
for em in emarkers:
e = em.get_event()
if e.moment_tensor:
a = kagan_angle(oevent.moment_tensor, e.moment_tensor)
self.kagan_angles[em] = a
else:
self.kagan_angles = {}
istart = self.index(0, _column_mapping['Dist [km]'])
istop = self.index(nmarkers - 1, _column_mapping['Kagan Angle [deg]'])
self.dataChanged.emit(istart, istop)
def plot_gnss(gnss_target, result, ifig, vertical=False):
campaign = gnss_target.campaign
item = PlotItem(
name='fig_%i' % ifig,
attributes={'targets': gnss_target.path},
title=u'Static GNSS Surface Displacements - Campaign %s' %
campaign.name,
description=u'''
Static surface displacement from GNSS campaign %s (black vectors) and
displacements derived from best model (red).
''' % campaign.name)
event = source.pyrocko_event()
locations = campaign.stations + [event]
lat, lon = od.geographic_midpoint_locations(locations)
if self.radius is None:
coords = num.array([loc.effective_latlon for loc in locations])
radius = od.distance_accurate50m_numpy(lat[num.newaxis],
lon[num.newaxis],
coords[:, 0].max(),
coords[:, 1]).max()
radius *= 1.1
if radius < 30. * km:
logger.warn('Radius of GNSS campaign %s too small, defaulting'
' to 30 km' % campaign.name)
radius = 30 * km
model_camp = gnss.GNSSCampaign(stations=copy.deepcopy(
campaign.stations),
name='grond model')
for ista, sta in enumerate(model_camp.stations):
sta.north.shift = result.statics_syn['displacement.n'][ista]
sta.north.sigma = 0.
sta.east.shift = result.statics_syn['displacement.e'][ista]
sta.east.sigma = 0.
if sta.up:
sta.up.shift = -result.statics_syn['displacement.d'][ista]
sta.up.sigma = 0.
m = automap.Map(width=self.size_cm[0],
height=self.size_cm[1],
lat=lat,
lon=lon,
radius=radius,
show_topo=self.show_topo,
show_grid=self.show_grid,
show_rivers=self.show_rivers,
color_wet=(216, 242, 254),
color_dry=(238, 236, 230))
all_stations = campaign.stations + model_camp.stations
offset_scale = num.zeros(len(all_stations))
for ista, sta in enumerate(all_stations):
for comp in sta.components.values():
offset_scale[ista] += comp.shift
offset_scale = num.sqrt(offset_scale**2).max()
m.add_gnss_campaign(campaign,
psxy_style={
'G': 'black',
'W': '0.8p,black',
},
offset_scale=offset_scale,
vertical=vertical)
m.add_gnss_campaign(model_camp,
psxy_style={
'G': 'red',
'W': '0.8p,red',
't': 30,
},
offset_scale=offset_scale,
vertical=vertical,
labels=False)
if isinstance(problem, CMTProblem) \
or isinstance(problem, VLVDProblem):
from pyrocko import moment_tensor
from pyrocko.plot import gmtpy
mt = event.moment_tensor.m_up_south_east()
ev_lat, ev_lon = event.effective_latlon
xx = num.trace(mt) / 3.
mc = num.matrix([[xx, 0., 0.], [0., xx, 0.], [0., 0., xx]])
mc = mt - mc
mc = mc / event.moment_tensor.scalar_moment() * \
moment_tensor.magnitude_to_moment(5.0)
m6 = tuple(moment_tensor.to6(mc))
symbol_size = 20.
m.gmt.psmeca(S='%s%g' % ('d', symbol_size / gmtpy.cm),
in_rows=[(ev_lon, ev_lat, 10) + m6 + (1, 0, 0)],
M=True,
*m.jxyr)
elif isinstance(problem, RectangularProblem):
m.gmt.psxy(in_rows=source.outline(cs='lonlat'),
L='+p2p,black',
W='1p,black',
G='black',
t=60,
*m.jxyr)
elif isinstance(problem, VolumePointProblem):
ev_lat, ev_lon = event.effective_latlon
dV = abs(source.volume_change)
sphere_radius = num.cbrt(dV / (4. / 3. * num.pi))
volcanic_circle = [ev_lon, ev_lat, '%fe' % sphere_radius]
m.gmt.psxy(S='E-',
in_rows=[volcanic_circle],
W='1p,black',
G='orange3',
*m.jxyr)
return (item, m)
def call(self):
try:
global vtk
import vtk
import sys
sys.path[0:0] = [self.module_dir()]
from grid_topo import setup_vtk_map_actor
sys.path[0:1] = []
except ImportError as _import_error:
self.fail('\nImportError:\n%s' % _import_error)
vtk = None
self.cleanup()
viewer = self.get_viewer()
stations = []
events = []
cone_actors = []
sphere_actors = []
if self.want_stations:
stations = self.get_stations()
if self.want_events:
markers = self.get_selected_event_markers()
if len(markers) == 0:
tmin, tmax = self.get_selected_time_range(fallback=True)
markers = filter(lambda x: tmin < x.tmin < tmax,
self.get_event_markers())
events = [m.get_event() for m in markers]
active_event = viewer.get_active_event()
to_rgba = ColorMapper('summer')
times = [e.time for e in events]
to_rgba.set_range(min(times), max(times))
if active_event:
to_rgba = ColorMapper('gray')
to_rgba.set_range(min(times), max(times))
for e in events:
e.get_vtk_color = to_rgba
if active_event:
def return_red(e):
return (1., 0., 0., 1.)
active_event.get_vtk_color = return_red
events.append(active_event)
all_lats = []
all_lons = []
for s in stations:
all_lats.append(s.lat)
all_lons.append(s.lon)
for e in events:
all_lats.append(e.lat)
all_lons.append(e.lon)
center_lat, center_lon = ortho.geographic_midpoint(
num.array(all_lats), num.array(all_lons))
center_lats = num.array([center_lat] * len(all_lats))
center_lons = num.array([center_lon] * len(all_lons))
distances = ortho.distance_accurate50m_numpy(num.array(all_lats),
num.array(all_lons),
center_lats, center_lons)
distance_max = num.max(distances)
size = distance_max / 50.
if len(events) != 0:
sphere_actors = events_to_vtksphere_actors(events,
z_scale=self.z_scale,
size=size / 2.)
if len(stations) != 0:
ns, es, depths = locations_to_ned(stations,
z_scale=self.z_scale,
has_elevation=True)
adata = num.array((es, ns, -depths)).flatten(order='F')
data = numpy_support.numpy_to_vtk(adata,
deep=True,
array_type=vtk.VTK_FLOAT)
data.SetNumberOfComponents(3)
cone_actors = self.stations_to_vtkcone_actors(data, size=size)
if self.want_topo:
distance_max += self.margin_radius * 1000.
topo_actor = setup_vtk_map_actor(center_lat,
center_lon,
distance_max,
super_elevation=self.z_scale,
decimation=int(self.z_decimation
or 1),
smoothing=self.smoothing)
self.frame = self.vtk_frame()
for actor in cone_actors:
actor.GetProperty().SetColor(0., 0., 1.)
self.frame.add_actor(actor)
for actor in sphere_actors:
self.frame.add_actor(actor)
if self.want_topo:
self.frame.add_actor(topo_actor)
self.frame.renderer.SetBackground(0.01, 0.05, 0.1)
self.frame.init()
