def set_event_relative_data(self, event, distance_3d=False):
surface_dist = orthodrome.distance_accurate50m(event, self)
if distance_3d:
dd = event.depth - self.depth
self.dist_m = math.sqrt(dd**2 + surface_dist**2)
else:
self.dist_m = surface_dist
self.dist_deg = surface_dist / orthodrome.earthradius_equator * orthodrome.r2d
self.azimuth = orthodrome.azimuth(event, self)
self.backazimuth = orthodrome.azimuth(self, event)
def set_event_relative_data(self, event, distance_3d=False):
surface_dist = orthodrome.distance_accurate50m(event, self)
if distance_3d:
dd = event.depth - self.depth
self.dist_m = math.sqrt(dd**2 + surface_dist**2)
else:
self.dist_m = surface_dist
self.dist_deg = surface_dist / orthodrome.earthradius_equator * \
orthodrome.r2d
self.azimuth = orthodrome.azimuth(event, self)
self.backazimuth = orthodrome.azimuth(self, event)
def assoicate_single(ev, data_dir, store_id, store,
stations=None, pre=0.5,
post=3, reference_event=None, min_len=420,
pick_sigma=0.02):
events = []
waveforms = []
labels = []
gf_freq = store.config.sample_rate
mod = store.config.earthmodel_1d
found = False
pathlist = Path(data_dir).glob('ev_*/')
for path in sorted(pathlist):
targets = []
path = str(path)+"/"
try:
event = model.load_events(path+"event.txt")[0]
if ev.time-10 < event.time and ev.time+10 > event.time:
traces_loaded = io.load(path+"/waveforms/rest/traces.mseed")
stations_unsorted = model.load_stations(data_dir+"stations.pf")
for st in stations_unsorted:
st.dist = orthodrome.distance_accurate50m(st.lat, st.lon,
event.lat,
event.lon)
st.azi = orthodrome.azimuth(st.lat, st.lon, event.lat,
event.lon)
stations = sorted(stations_unsorted, key=lambda x: x.dist,
reverse=True)
traces_processed = []
traces = wp.check_traces(traces_loaded, stations, min_len=min_len)
traces_processed, nsamples = wp.process_loaded_waveforms(traces,
stations,
ev,
gf_freq,
mod,
pre,
post)
if found is False:
events.append(event)
waveforms.append(traces_processed)
found = True
except:
pass
data_events, nsamples = wp.prepare_waveforms(waveforms)
return data_events, nsamples, event
def prep_data_batch(data_dir, store_id, stations=None, pre=0.5,
post=3, reference_event=None, min_len=420,
pick_sigma=0.02):
engine = LocalEngine(store_superdirs=['/home/asteinbe/gf_stores'])
store = engine.get_store(store_id)
mod = store.config.earthmodel_1d
gf_freq = store.config.sample_rate
cake_phase = cake.PhaseDef("P")
phase_list = [cake_phase]
events = []
waveforms = []
waveforms_shifted = []
events = scedc_util.scedc_fm_to_pyrocko(file)
labels = labels_from_events(events)
pathlist = Path(data_dir).glob('ev_0/')
for path in sorted(pathlist):
try:
targets = []
path = str(path)+"/"
event = model.load_events(path+"event.txt")[0]
traces_loaded = io.load(path+"traces.mseed")
stations_unsorted = model.load_stations(data_dir+"stations.pf")
for st in stations_unsorted:
st.dist = orthodrome.distance_accurate50m(st.lat, st.lon,
event.lat,
event.lon)
st.azi = orthodrome.azimuth(st.lat, st.lon, event.lat,
event.lon)
stations = sorted(stations_unsorted, key=lambda x: x.dist,
reverse=True)
traces_processed = []
traces = check_traces(traces_loaded, stations, min_len=min_len)
traces_processed, nsamples = wp.process_loaded_waveforms(traces,
stations,
event,
gf_freq,
mod,
pre, post)
events.append(event)
waveforms.append(traces_processed)
except:
pass
return waveforms, nsamples, events, waveforms_shifted
def set_event_relative_data( self, event ):
self.dist_m = orthodrome.distance_accurate50m( event, self )
self.dist_deg = self.dist_m / orthodrome.earthradius_equator *orthodrome.r2d
self.azimuth = orthodrome.azimuth(event, self)
self.backazimuth = orthodrome.azimuth(self, event)
def process(self,
event,
timing,
bazi=None,
slow=None,
restitute=False,
*args,
**kwargs):
'''
:param timing: CakeTiming. Uses the definition without the offset.
:param fn_dump_center: filename to where center stations shall be dumped
:param fn_beam: filename of beam trace
:param model: earthmodel to use(optional)
:param earthmodel to use(optional)
:param network: network code(optional)
:param station: station code(optional)
'''
logger.debug('start beam forming')
stations = self.stations
network_code = kwargs.get('responses', None)
network_code = kwargs.get('network', '')
station_code = kwargs.get('station', 'STK')
c_station_id = (network_code, station_code)
t_shifts = []
lat_c, lon_c, z_c = self.c_lat_lon_z
self.station_c = Station(lat=float(lat_c),
lon=float(lon_c),
elevation=float(z_c),
depth=0.,
name='Array Center',
network=c_station_id[0],
station=c_station_id[1][:5])
fn_dump_center = kwargs.get('fn_dump_center', 'array_center.pf')
fn_beam = kwargs.get('fn_beam', 'beam.mseed')
if event:
mod = cake.load_model(crust2_profile=(event.lat, event.lon))
dist = ortho.distance_accurate50m(event, self.station_c)
ray = timing.t(mod, (event.depth, dist), get_ray=True)
if ray is None:
logger.error(
'None of defined phases available at beam station:\n %s' %
self.station_c)
return
else:
b = ortho.azimuth(self.station_c, event)
if b >= 0.:
self.bazi = b
elif b < 0.:
self.bazi = 360. + b
self.slow = ray.p / (cake.r2d * cake.d2m)
else:
self.bazi = bazi
self.slow = slow
logger.info(
'stacking %s with slowness %1.4f s/km at back azimut %1.1f '
'degrees' %
('.'.join(c_station_id), self.slow * cake.km, self.bazi))
lat0 = num.array([lat_c] * len(stations))
lon0 = num.array([lon_c] * len(stations))
lats = num.array([s.lat for s in stations])
lons = num.array([s.lon for s in stations])
ns, es = ortho.latlon_to_ne_numpy(lat0, lon0, lats, lons)
theta = num.float(self.bazi * num.pi / 180.)
R = num.array([[num.cos(theta), -num.sin(theta)],
[num.sin(theta), num.cos(theta)]])
distances = R.dot(num.vstack((es, ns)))[1]
channels = set()
self.stacked = {}
num_stacked = {}
self.t_shifts = {}
self.shifted_traces = []
taperer = trace.CosFader(xfrac=0.05)
if self.diff_dt_treat == 'downsample':
self.traces.sort(key=lambda x: x.deltat)
elif self.diff_dt_treat == 'oversample':
dts = [t.deltat for t in self.traces]
for tr in self.traces:
tr.resample(min(dts))
for tr in self.traces:
if tr.nslc_id[:2] == c_station_id:
continue
tr = tr.copy(data=True)
tr.ydata = tr.ydata.astype(
num.float64) - tr.ydata.mean(dtype=num.float64)
tr.taper(taperer)
try:
stack_trace = self.stacked[tr.channel]
num_stacked[tr.channel] += 1
except KeyError:
stack_trace = tr.copy(data=True)
stack_trace.set_ydata(num.zeros(len(stack_trace.get_ydata())))
stack_trace.set_codes(network=c_station_id[0],
station=c_station_id[1],
location='',
channel=tr.channel)
self.stacked[tr.channel] = stack_trace
channels.add(tr.channel)
num_stacked[tr.channel] = 1
nslc_id = tr.nslc_id
try:
stats = list(
filter(
lambda x: util.match_nslc('%s.%s.%s.*' % x.nsl(),
nslc_id), stations))
stat = stats[0]
except IndexError:
break
i = stations.index(stat)
d = distances[i]
t_shift = d * self.slow
t_shifts.append(t_shift)
tr.shift(t_shift)
self.t_shifts[tr.nslc_id[:2]] = t_shift
if self.normalize_std:
tr.ydata = tr.ydata / tr.ydata.std()
if num.abs(tr.deltat - stack_trace.deltat) > 0.000001:
if self.diff_dt_treat == 'downsample':
stack_trace.downsample_to(tr.deltat)
elif self.diff_dt_treat == 'upsample':
raise Exception(
'something went wrong with the upsampling, previously')
stack_trace.add(tr)
self.shifted_traces.append(tr)
if self.post_normalize:
for ch, tr in self.stacked.items():
tr.set_ydata(tr.get_ydata() / num_stacked[ch])
self.save_station(fn_dump_center)
self.checked_nslc([stack_trace])
self.save(stack_trace, fn_beam)
return self.shifted_traces, stack_trace, t_shifts
color_t=(0.3, 0.3, 0.8),
projection=projection,
size_units='data')
for rlat, rlon in rlatlons:
distance = orthodrome.distance_accurate50m(slat, slon, rlat, rlon)
rays = mod.arrivals(phases=cake.PhaseDef('P'),
zstart=sdepth,
zstop=rdepth,
distances=[distance * cake.m2d])
if not rays:
continue
takeoff = rays[0].takeoff_angle()
azi = orthodrome.azimuth(slat, slon, rlat, rlon)
# to spherical coordinates, r, theta, phi in radians
rtp = num.array([[1., num.deg2rad(takeoff), num.deg2rad(90. - azi)]])
# to 3D coordinates (x, y, z)
points = beachball.numpy_rtp2xyz(rtp)
# project to 2D with same projection as used in beachball
x, y = beachball.project(points, projection=projection).T
axes.plot(x, y, '+', ms=10., mew=2.0, mec='black', mfc='none')
#fig.savefig('beachball-example04.png')
plt.show()
def load_data(data_dir,
store_id,
stations=None,
pre=0.5,
post=3,
reference_event=None,
min_len=420,
error_t=None,
lat=None,
lon=None,
depth=None,
engine=None,
ev_id=None):
store = engine.get_store(store_id)
mod = store.config.earthmodel_1d
gf_freq = store.config.sample_rate
cake_phase = cake.PhaseDef("P")
phase_list = [cake_phase]
events = []
waveforms = []
waveforms_shifted = []
if ev_id is None:
pathlist = Path(data_dir).glob('ev_*/')
else:
pathlist = Path(data_dir).glob('ev_%s/' % ev_id)
for path in sorted(pathlist):
targets = []
path = str(path) + "/"
event = model.load_events(path + "event.txt")[0]
traces_loaded = io.load(path + "waveforms/rest/traces.mseed")
stations_unsorted = model.load_stations(data_dir + "stations.pf")
for st in stations_unsorted:
st.dist = orthodrome.distance_accurate50m(st.lat, st.lon,
event.lat, event.lon)
st.azi = orthodrome.azimuth(st.lat, st.lon, event.lat, event.lon)
stations = sorted(stations_unsorted,
key=lambda x: x.dist,
reverse=True)
traces_processed = []
if lat is not None:
event.lat = lat
event.lon = lon
event.depth = depth
traces = check_traces(traces_loaded,
stations,
min_len=min_len,
event=event)
if error_t is not None:
traces_shift = copy.deepcopy(traces)
traces_processed, nsamples = process_loaded_waveforms(
traces, stations, event, gf_freq, mod, pre, post)
if error_t is not None:
traces_processed_shifted = process_loaded_waveforms_shift(
traces_shift,
stations,
event,
gf_freq,
mod,
pre,
post,
shift_max=error_t)
waveforms_shifted.append(traces_processed_shifted)
events.append(event)
waveforms.append(traces_processed)
return waveforms, nsamples, events, waveforms_shifted
def generate_test_data_grid(store_id,
store_dirs,
coordinates,
geometry_params,
pre=0.5,
post=3,
stations_input=None,
batch_loading=256,
paths_disks=None):
engine = LocalEngine(store_superdirs=[store_dirs])
store = engine.get_store(store_id)
mod = store.config.earthmodel_1d
cake_phase = cake.PhaseDef("P")
phase_list = [cake_phase]
waveforms_events = []
waveforms_events_uncut = []
waveforms_noise = []
sources = []
lats = coordinates[0]
lons = coordinates[1]
depths = coordinates[2]
if stations_input is None:
stations_unsorted = model.load_stations("data/stations.pf")
else:
stations_unsorted = model.load_stations(stations_input)
for st in stations_unsorted:
st.dist = orthodrome.distance_accurate50m(st.lat, st.lon, lats[0],
lons[0])
st.azi = orthodrome.azimuth(st.lat, st.lon, lats[0], lons[0])
stations = sorted(stations_unsorted, key=lambda x: x.dist, reverse=True)
targets = []
events = []
mean_lat = []
mean_lon = []
max_rho = 0.
for st in stations:
mean_lat.append(st.lat)
mean_lon.append(st.lon)
for cha in st.channels:
if cha.name is not "R" and cha.name is not "T" and cha.name is not "Z":
target = Target(lat=st.lat,
lon=st.lon,
store_id=store_id,
interpolation='multilinear',
quantity='displacement',
codes=st.nsl() + (cha.name, ))
targets.append(target)
strikes = geometry_params[0]
dips = geometry_params[1]
rakes = geometry_params[2]
vs = geometry_params[3]
ws = geometry_params[4]
grid_points = []
for lat in lats:
for lon in lons:
for depth in depths:
grid_points.append([lat, lon, depth])
ray.init(num_cpus=num_cpus - 1)
npm = len(lats) * len(lons) * len(depths)
npm_geom = len(strikes) * len(dips) * len(rakes)
results = ray.get([
get_parallel_mtqt.remote(i,
targets,
store_id,
post,
pre,
stations,
mod,
grid_points[i],
strikes,
dips,
rakes,
vs,
ws,
store_dirs,
batch_loading=batch_loading,
npm=npm_geom,
paths_disks=paths_disks)
for i in range(len(grid_points))
])
ray.shutdown()
return waveforms_events
def set_event_relative_data(self, event):
self.dist_m = orthodrome.distance_accurate50m(event, self)
self.dist_deg = self.dist_m / orthodrome.earthradius_equator * orthodrome.r2d
self.azimuth = orthodrome.azimuth(event, self)
self.backazimuth = orthodrome.azimuth(self, event)
def call(self):
self.cleanup()
viewer = self.get_viewer()
master = viewer.get_active_event()
if master is None:
self.fail('no master event selected')
stations = list(viewer.stations.values())
stations.sort(key=lambda s: (s.network,s.station))
if not stations:
self.fail('no station information available')
# gather events to be processed
events = []
for m in viewer.markers:
if isinstance(m, EventMarker):
if m.kind == 0:
events.append( m.get_event() )
events.sort(key=lambda ev: ev.time)
event_to_number = {}
for iev, ev in enumerate(events):
event_to_number[ev] = iev
if self.model_select.startswith('Global'):
model_key = 'global'
else:
model_key = master.lat, master.lon
if model_key != self.model_key:
if self.model_select.startswith('Global'):
self.model = cake.load_model()
else:
latlon = master.lat, master.lon
profile = crust2x2.get_profile(*latlon)
profile.set_layer_thickness(crust2x2.LWATER, 0.0)
self.model = cake.LayeredModel.from_scanlines(
cake.from_crust2x2_profile(profile))
self.model_key = model_key
phases = {
'P': ([ cake.PhaseDef(x) for x in 'P p'.split() ], 'Z'),
'S': ([ cake.PhaseDef(x) for x in 'S s'.split() ], 'NE'),
}
phasenames = phases.keys()
phasenames.sort()
# synthetic arrivals and ray geometry for master event
master_depth = master.depth
if self.master_depth_km is not None:
master_depth = self.master_depth_km * km
tt = {}
g = {}
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
dist = orthodrome.distance_accurate50m(master, station)
azi = orthodrome.azimuth(master, station)
arrivals = self.model.arrivals(
phases=phases[phasename][0],
distances=[ dist*cake.m2d ],
zstart = master_depth,
zstop = 0.0)
if arrivals:
first = arrivals[0]
tt[station.network, station.station, phasename] = first.t
takeoff = first.takeoff_angle()
u = first.path.first_straight().u_in(first.endgaps)
g[iphase, istation] = num.array([
math.cos(azi*d2r) * math.sin(takeoff*d2r) * u,
math.sin(azi*d2r) * math.sin(takeoff*d2r) * u,
math.cos(takeoff*d2r) * u ])
# gather picks for each event
for ev in events:
picks = {}
for m2 in viewer.markers:
if isinstance(m2, PhaseMarker) and m2.kind == 0:
if m2.get_event() == ev:
net, sta, _, _ = m2.one_nslc()
picks[net,sta,m2.get_phasename()] = (m2.tmax + m2.tmin) / 2.0
ev.picks = picks
# time corrections for extraction windows
dataobs = []
datasyn = []
for phasename in phasenames:
for station in stations:
nsp = station.network, station.station, phasename
datasyn.append(tt.get(nsp,None))
for ev in events:
if nsp in ev.picks:
ttobs = ev.picks[nsp] - ev.time
else:
ttobs = None
dataobs.append(ttobs)
ttsyn = num.array(datasyn, dtype=num.float).reshape((
len(phasenames),
len(stations)))
ttobs = num.array(dataobs, dtype=num.float).reshape((
len(phasenames),
len(stations),
len(events)))
ttres = ttobs - ttsyn[:,:,num.newaxis]
tt_corr_event = num.nansum( ttres, axis=1) / \
num.nansum( num.isfinite(ttres), axis=1 )
tt_corr_event = num.where(num.isfinite(tt_corr_event), tt_corr_event, 0.)
ttres -= tt_corr_event[:,num.newaxis,:]
tt_corr_station = num.nansum( ttres, axis=2) / \
num.nansum( num.isfinite(ttres), axis=2 )
tt_corr_station = num.where(num.isfinite(tt_corr_station), tt_corr_station, 0.)
ttres -= tt_corr_station[:,:, num.newaxis]
tevents_raw = num.array( [ ev.time for ev in events ] )
tevents_corr = tevents_raw + num.mean(tt_corr_event, axis=0)
# print timing information
print 'timing stats'
for iphasename, phasename in enumerate(phasenames):
data = []
for ev in events:
iev = event_to_number[ev]
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
if nsp in tt and nsp in ev.picks:
tarr = ev.time + tt[nsp]
tarr_ec = tarr + tt_corr_event[iphasename, iev]
tarr_ec_sc = tarr_ec + tt_corr_station[iphasename, istation]
tobs = ev.picks[nsp]
data.append((tobs-tarr, tobs-tarr_ec, tobs-tarr_ec_sc))
if data:
data = num.array(data, dtype=num.float).T
print 'event %10s %3s %3i %15.2g %15.2g %15.2g' % (
(ev.name, phasename, data.shape[1]) +
tuple( num.mean(num.abs(x)) for x in data ))
else:
print 'event %10s %3s no picks' % (ev.name, phasename)
# extract and preprocess waveforms
tpad = 0.0
for f in self.corner_highpass, self.corner_lowpass:
if f is not None:
tpad = max(tpad, 1.0/f)
pile = self.get_pile()
waveforms = {}
for ev in events:
iev = event_to_number[ev]
markers = []
for iphasename, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
if nsp in tt:
tarr = ev.time + tt[nsp]
nslcs = [ ( station.network, station.station, '*', '*' ) ]
marker = PhaseMarker( nslcs, tarr, tarr, 1, event=ev,
phasename=phasename)
markers.append(marker)
tarr2 = tarr + tt_corr_station[iphasename, istation] + \
tt_corr_event[iphasename, iev]
marker = PhaseMarker( nslcs, tarr2, tarr2, 2, event=ev,
phasename=phasename)
markers.append(marker)
tmin = tarr2+self.tstart
tmax = tarr2+self.tend
marker = PhaseMarker( nslcs,
tmin, tmax, 3, event=ev,
phasename=phasename)
markers.append(marker)
trs = pile.all(tmin, tmax, tpad=tpad, trace_selector=
lambda tr: tr.nslc_id[:2] == nsp[:2],
want_incomplete=False)
trok = []
for tr in trs:
if num.all(tr.ydata[0] == tr.ydata):
continue
if self.corner_highpass:
tr.highpass(4, self.corner_highpass)
if self.corner_lowpass:
tr.lowpass(4, self.corner_lowpass)
tr.chop(tmin, tmax)
tr.set_location(ev.name)
#tr.shift( - (tmin - master.time) )
if num.all(num.isfinite(tr.ydata)):
trok.append(tr)
waveforms[nsp+(iev,)] = trok
self.add_markers(markers)
def get_channel(trs, cha):
for tr in trs:
if tr.channel == cha:
return tr
return None
nevents = len(events)
nstations = len(stations)
nphases = len(phasenames)
# correlate waveforms
coefs = num.zeros((nphases, nstations, nevents, nevents))
coefs.fill(num.nan)
tshifts = coefs.copy()
tshifts_picked = coefs.copy()
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
for a in events:
ia = event_to_number[a]
for b in events:
ib = event_to_number[b]
if ia == ib:
continue
if nsp in a.picks and nsp in b.picks:
tshifts_picked[iphase,istation,ia,ib] = \
b.picks[nsp] - a.picks[nsp]
wa = waveforms[nsp+(ia,)]
wb = waveforms[nsp+(ib,)]
channels = list(set([ tr.channel for tr in wa + wb ]))
channels.sort()
tccs = []
for cha in channels:
if cha[-1] not in phases[phasename][1]:
continue
ta = get_channel(wa, cha)
tb = get_channel(wb, cha)
if ta is None or tb is None:
continue
tcc = trace.correlate(ta,tb, mode='full', normalization='normal',
use_fft=True)
tccs.append(tcc)
if not tccs:
continue
tc = None
for tcc in tccs:
if tc is None:
tc = tcc
else:
tc.add(tcc)
tc.ydata *= 1./len(tccs)
tmid = tc.tmin*0.5 + tc.tmax*0.5
tlen = (tc.tmax - tc.tmin)*0.5
tc_cut = tc.chop(tmid-tlen*0.5, tmid+tlen*0.5, inplace=False)
tshift, coef = tc_cut.max()
if (tshift < tc.tmin + 0.5*tc.deltat or
tc.tmax - 0.5*tc.deltat < tshift):
continue
coefs[iphase,istation,ia,ib] = coef
tshifts[iphase,istation,ia,ib] = tshift
if self.show_correlation_traces:
tc.shift(master.time - (tc.tmax + tc.tmin)/2.)
self.add_trace(tc)
#tshifts = tshifts_picked
coefssum_sta = num.nansum(coefs, axis=2) / num.sum(num.isfinite(coefs), axis=2)
csum_sta = num.nansum(coefssum_sta, axis=2) / num.sum(num.isfinite(coefssum_sta), axis=2)
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
print 'station %-5s %s %15.2g' % (station.station, phasename, csum_sta[iphase,istation])
coefssum = num.nansum(coefs, axis=1) / num.sum(num.isfinite(coefs), axis=1)
csumevent = num.nansum(coefssum, axis=2) / num.sum(num.isfinite(coefssum), axis=2)
above = num.where(num.isfinite(coefs), coefs >= self.min_corr, 0)
csumabove = num.sum(num.sum(above, axis=1), axis=2)
coefssum = num.ma.masked_invalid(coefssum)
print 'correlation stats'
for iphase, phasename in enumerate(phasenames):
for ievent, event in enumerate(events):
print 'event %10s %3s %8i %15.2g' % (
event.name, phasename,
csumabove[iphase,ievent], csumevent[iphase,ievent])
# plot event correlation matrix
fframe = self.figure_frame()
fig = fframe.gcf()
for iphase, phasename in enumerate(phasenames):
p = fig.add_subplot(1,nphases,iphase+1)
p.set_xlabel('Event number')
p.set_ylabel('Event number')
mesh = p.pcolormesh(coefssum[iphase])
cb = fig.colorbar(mesh, ax=p)
cb.set_label('Max correlation coefficient')
if self.save:
fig.savefig(self.output_filename(dir='correlation.pdf'))
fig.canvas.draw()
# setup and solve linear system
data = []
rows = []
weights = []
for iphase in xrange(nphases):
for istation in xrange(nstations):
for ia in xrange(nevents):
for ib in xrange(ia+1,nevents):
k = iphase, istation, ia, ib
w = coefs[k]
if not num.isfinite(tshifts[k]) \
or not num.isfinite(w) or w < self.min_corr:
continue
row = num.zeros(nevents*4)
row[ia*4:ia*4+3] = g[iphase,istation]
row[ia*4+3] = -1.0
row[ib*4:ib*4+3] = -g[iphase,istation]
row[ib*4+3] = 1.0
weights.append(w)
rows.append(row)
data.append(tshifts[iphase,istation,ia,ib])
nsamp = len(data)
for i in range(4):
row = num.zeros(nevents*4)
row[i::4] = 1.
rows.append(row)
data.append(0.0)
if self.fix_depth:
for ievent in range(nevents):
row = num.zeros(nevents*4)
row[ievent*4+2] = 1.0
rows.append(row)
data.append(0.0)
a = num.array(rows, dtype=num.float)
d = num.array(data, dtype=num.float)
w = num.array(weights, dtype=num.float)
if self.weighting == 'equal':
w[:nsamp] = 1.0
elif self.weighting == 'linear':
pass
elif self.weighting == 'quadratic':
w[:nsamp] = w[:nsamp]**2
a[:nsamp,:] *= w[:,num.newaxis]
d[:nsamp] *= w[:nsamp]
x, residuals, rank, singular = num.linalg.lstsq(a,d)
x0 = num.zeros(nevents*4)
x0[3::4] = tevents_corr
mean_abs_residual0 = num.mean(
num.abs((num.dot(a[:nsamp], x0) - d[:nsamp])/w[:nsamp]))
mean_abs_residual = num.mean(
num.abs((num.dot(a[:nsamp],x) - d[:nsamp])/w[:nsamp]))
print mean_abs_residual0, mean_abs_residual
# distorted solutions
npermutations = 100
noiseamount = mean_abs_residual
xdistorteds = []
for i in range(npermutations):
dnoisy = d.copy()
dnoisy[:nsamp] += num.random.normal(size=nsamp)*noiseamount*w[:nsamp]
xdistorted, residuals, rank, singular = num.linalg.lstsq(a,dnoisy)
xdistorteds.append(xdistorted)
mean_abs_residual = num.mean(num.abs(num.dot(a,xdistorted)[:nsamp] - dnoisy[:nsamp]))
tmean = num.mean([ e.time for e in events ])
north = x[0::4]
east = x[1::4]
down = x[2::4]
etime = x[3::4] + tmean
def plot_range(x):
mi, ma = num.percentile(x, [10., 90.])
ext = (ma-mi)/5.
mi -= ext
ma += ext
return mi, ma
lat, lon = orthodrome.ne_to_latlon(master.lat, master.lon, north, east)
events_out = []
for ievent, event in enumerate(events):
event_out = model.Event(time=etime[ievent],
lat=lat[ievent],
lon=lon[ievent],
depth=down[ievent] + master_depth,
name = event.name)
mark = EventMarker(event_out, kind=4)
self.add_marker(mark)
events_out.append(event_out)
model.Event.dump_catalog(events_out, 'events.relocated.txt')
# plot results
ned_orig = []
for event in events:
n, e = orthodrome.latlon_to_ne(master, event)
d = event.depth
ned_orig.append((n,e,d))
ned_orig = num.array(ned_orig)
ned_orig[:,0] -= num.mean(ned_orig[:,0])
ned_orig[:,1] -= num.mean(ned_orig[:,1])
ned_orig[:,2] -= num.mean(ned_orig[:,2])
north0, east0, down0 = ned_orig.T
north2, east2, down2, time2 = num.hstack(xdistorteds).reshape((-1,4)).T
fframe = self.figure_frame()
fig = fframe.gcf()
color_sym = (0.1,0.1,0.0)
color_scat = (0.3,0.5,1.0,0.2)
d = u'\u0394 '
if not self.fix_depth:
p = fig.add_subplot(2,2,1, aspect=1.0)
else:
p = fig.add_subplot(1,1,1, aspect=1.0)
mi_north, ma_north = plot_range(north)
mi_east, ma_east = plot_range(east)
mi_down, ma_down = plot_range(down)
p.set_xlabel(d+'East [km]')
p.set_ylabel(d+'North [km]')
p.plot(east2/km, north2/km, '.', color=color_scat, markersize=2)
p.plot(east/km, north/km, '+', color=color_sym)
p.plot(east0/km, north0/km, 'x', color=color_sym)
p0 = p
for i,ev in enumerate(events):
p.text(east[i]/km, north[i]/km, ev.name, clip_on=True)
if not self.fix_depth:
p = fig.add_subplot(2,2,2, sharey=p0, aspect=1.0)
p.set_xlabel(d+'Depth [km]')
p.set_ylabel(d+'North [km]')
p.plot(down2/km, north2/km, '.', color=color_scat, markersize=2)
p.plot(down/km, north/km, '+', color=color_sym)
for i,ev in enumerate(events):
p.text(down[i]/km, north[i]/km, ev.name, clip_on=True)
p1 = p
p = fig.add_subplot(2,2,3, sharex=p0, aspect=1.0)
p.set_xlabel(d+'East [km]')
p.set_ylabel(d+'Depth [km]')
p.plot(east2/km, down2/km, '.', color=color_scat, markersize=2)
p.plot(east/km, down/km, '+', color=color_sym)
for i,ev in enumerate(events):
p.text(east[i]/km, down[i]/km, ev.name, clip_on=True)
p.invert_yaxis()
p2 = p
p0.set_xlim(mi_east/km, ma_east/km)
p0.set_ylim(mi_north/km, ma_north/km)
if not self.fix_depth:
p1.set_xlim(mi_down/km, ma_down/km)
p2.set_ylim(mi_down/km, ma_down/km)
if self.save:
fig.savefig(self.output_filename(dir='locations.pdf'))
fig.canvas.draw()
def call(self):
self.cleanup()
viewer = self.get_viewer()
master = viewer.get_active_event()
if master is None:
self.fail('no master event selected')
stations = list(viewer.stations.values())
stations.sort(key=lambda s: (s.network, s.station))
if not stations:
self.fail('no station information available')
# gather events to be processed
events = []
for m in viewer.markers:
if isinstance(m, EventMarker):
if m.kind == 0:
events.append(m.get_event())
events.sort(key=lambda ev: ev.time)
event_to_number = {}
for iev, ev in enumerate(events):
event_to_number[ev] = iev
if self.model_select.startswith('Global'):
model_key = 'global'
else:
model_key = master.lat, master.lon
if model_key != self.model_key:
if self.model_select.startswith('Global'):
self.model = cake.load_model()
else:
latlon = master.lat, master.lon
profile = crust2x2.get_profile(*latlon)
profile.set_layer_thickness(crust2x2.LWATER, 0.0)
self.model = cake.LayeredModel.from_scanlines(
cake.from_crust2x2_profile(profile))
self.model_key = model_key
phases = {
'P': ([cake.PhaseDef(x) for x in 'P p'.split()], 'Z'),
'S': ([cake.PhaseDef(x) for x in 'S s'.split()], 'NE'),
}
phasenames = list(phases.keys())
phasenames.sort()
# synthetic arrivals and ray geometry for master event
master_depth = master.depth
if self.master_depth_km is not None:
master_depth = self.master_depth_km * km
tt = {}
g = {}
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
dist = orthodrome.distance_accurate50m(master, station)
azi = orthodrome.azimuth(master, station)
arrivals = self.model.arrivals(phases=phases[phasename][0],
distances=[dist * cake.m2d],
zstart=master_depth,
zstop=0.0)
if arrivals:
first = arrivals[0]
tt[station.network, station.station, phasename] = first.t
takeoff = first.takeoff_angle()
u = first.path.first_straight().u_in(first.endgaps)
g[iphase, istation] = num.array([
math.cos(azi * d2r) * math.sin(takeoff * d2r) * u,
math.sin(azi * d2r) * math.sin(takeoff * d2r) * u,
math.cos(takeoff * d2r) * u
])
# gather picks for each event
for ev in events:
picks = {}
for m2 in viewer.markers:
if isinstance(m2, PhaseMarker) and m2.kind == 0:
if m2.get_event() == ev:
net, sta, _, _ = m2.one_nslc()
picks[net, sta,
m2.get_phasename()] = (m2.tmax + m2.tmin) / 2.0
ev.picks = picks
# time corrections for extraction windows
dataobs = []
datasyn = []
for phasename in phasenames:
for station in stations:
nsp = station.network, station.station, phasename
datasyn.append(tt.get(nsp, None))
for ev in events:
if nsp in ev.picks:
ttobs = ev.picks[nsp] - ev.time
else:
ttobs = None
dataobs.append(ttobs)
ttsyn = num.array(datasyn, dtype=num.float).reshape(
(len(phasenames), len(stations)))
ttobs = num.array(dataobs, dtype=num.float).reshape(
(len(phasenames), len(stations), len(events)))
ttres = ttobs - ttsyn[:, :, num.newaxis]
tt_corr_event = num.nansum( ttres, axis=1) / \
num.nansum( num.isfinite(ttres), axis=1 )
tt_corr_event = num.where(num.isfinite(tt_corr_event), tt_corr_event,
0.)
ttres -= tt_corr_event[:, num.newaxis, :]
tt_corr_station = num.nansum( ttres, axis=2) / \
num.nansum( num.isfinite(ttres), axis=2 )
tt_corr_station = num.where(num.isfinite(tt_corr_station),
tt_corr_station, 0.)
ttres -= tt_corr_station[:, :, num.newaxis]
tevents_raw = num.array([ev.time for ev in events])
tevents_corr = tevents_raw + num.mean(tt_corr_event, axis=0)
# print timing information
print('timing stats')
for iphasename, phasename in enumerate(phasenames):
data = []
for ev in events:
iev = event_to_number[ev]
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
if nsp in tt and nsp in ev.picks:
tarr = ev.time + tt[nsp]
tarr_ec = tarr + tt_corr_event[iphasename, iev]
tarr_ec_sc = tarr_ec + tt_corr_station[iphasename,
istation]
tobs = ev.picks[nsp]
data.append(
(tobs - tarr, tobs - tarr_ec, tobs - tarr_ec_sc))
if data:
data = num.array(data, dtype=num.float).T
print('event %10s %3s %3i %15.2g %15.2g %15.2g' %
((ev.name, phasename, data.shape[1]) +
tuple(num.mean(num.abs(x)) for x in data)))
else:
print('event %10s %3s no picks' % (ev.name, phasename))
# extract and preprocess waveforms
tpad = 0.0
for f in self.corner_highpass, self.corner_lowpass:
if f is not None:
tpad = max(tpad, 1.0 / f)
pile = self.get_pile()
waveforms = {}
for ev in events:
iev = event_to_number[ev]
markers = []
for iphasename, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
if nsp in tt:
tarr = ev.time + tt[nsp]
nslcs = [(station.network, station.station, '*', '*')]
marker = PhaseMarker(nslcs,
tarr,
tarr,
1,
event=ev,
phasename=phasename)
markers.append(marker)
tarr2 = tarr + tt_corr_station[iphasename, istation] + \
tt_corr_event[iphasename, iev]
marker = PhaseMarker(nslcs,
tarr2,
tarr2,
2,
event=ev,
phasename=phasename)
markers.append(marker)
tmin = tarr2 + self.tstart
tmax = tarr2 + self.tend
marker = PhaseMarker(nslcs,
tmin,
tmax,
3,
event=ev,
phasename=phasename)
markers.append(marker)
trs = pile.all(tmin,
tmax,
tpad=tpad,
trace_selector=lambda tr: tr.nslc_id[:2]
== nsp[:2],
want_incomplete=False)
trok = []
for tr in trs:
if num.all(tr.ydata[0] == tr.ydata):
continue
if self.corner_highpass:
tr.highpass(4, self.corner_highpass)
if self.corner_lowpass:
tr.lowpass(4, self.corner_lowpass)
tr.chop(tmin, tmax)
tr.set_location(ev.name)
#tr.shift( - (tmin - master.time) )
if num.all(num.isfinite(tr.ydata)):
trok.append(tr)
waveforms[nsp + (iev, )] = trok
self.add_markers(markers)
def get_channel(trs, cha):
for tr in trs:
if tr.channel == cha:
return tr
return None
nevents = len(events)
nstations = len(stations)
nphases = len(phasenames)
# correlate waveforms
coefs = num.zeros((nphases, nstations, nevents, nevents))
coefs.fill(num.nan)
tshifts = coefs.copy()
tshifts_picked = coefs.copy()
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
nsp = station.network, station.station, phasename
for a in events:
ia = event_to_number[a]
for b in events:
ib = event_to_number[b]
if ia == ib:
continue
if nsp in a.picks and nsp in b.picks:
tshifts_picked[iphase,istation,ia,ib] = \
b.picks[nsp] - a.picks[nsp]
wa = waveforms[nsp + (ia, )]
wb = waveforms[nsp + (ib, )]
channels = list(set([tr.channel for tr in wa + wb]))
channels.sort()
tccs = []
for cha in channels:
if cha[-1] not in phases[phasename][1]:
continue
ta = get_channel(wa, cha)
tb = get_channel(wb, cha)
if ta is None or tb is None:
continue
tcc = trace.correlate(ta,
tb,
mode='full',
normalization='normal',
use_fft=True)
tccs.append(tcc)
if not tccs:
continue
tc = None
for tcc in tccs:
if tc is None:
tc = tcc
else:
tc.add(tcc)
tc.ydata *= 1. / len(tccs)
tmid = tc.tmin * 0.5 + tc.tmax * 0.5
tlen = (tc.tmax - tc.tmin) * 0.5
tc_cut = tc.chop(tmid - tlen * 0.5,
tmid + tlen * 0.5,
inplace=False)
tshift, coef = tc_cut.max()
if (tshift < tc.tmin + 0.5 * tc.deltat
or tc.tmax - 0.5 * tc.deltat < tshift):
continue
coefs[iphase, istation, ia, ib] = coef
tshifts[iphase, istation, ia, ib] = tshift
if self.show_correlation_traces:
tc.shift(master.time - (tc.tmax + tc.tmin) / 2.)
self.add_trace(tc)
#tshifts = tshifts_picked
coefssum_sta = num.nansum(coefs, axis=2) / num.sum(num.isfinite(coefs),
axis=2)
csum_sta = num.nansum(coefssum_sta, axis=2) / num.sum(
num.isfinite(coefssum_sta), axis=2)
for iphase, phasename in enumerate(phasenames):
for istation, station in enumerate(stations):
print('station %-5s %s %15.2g' %
(station.station, phasename, csum_sta[iphase, istation]))
coefssum = num.nansum(coefs, axis=1) / num.sum(num.isfinite(coefs),
axis=1)
csumevent = num.nansum(coefssum, axis=2) / num.sum(
num.isfinite(coefssum), axis=2)
above = num.where(num.isfinite(coefs), coefs >= self.min_corr, 0)
csumabove = num.sum(num.sum(above, axis=1), axis=2)
coefssum = num.ma.masked_invalid(coefssum)
print('correlation stats')
for iphase, phasename in enumerate(phasenames):
for ievent, event in enumerate(events):
print('event %10s %3s %8i %15.2g' %
(event.name, phasename, csumabove[iphase, ievent],
csumevent[iphase, ievent]))
# plot event correlation matrix
fframe = self.figure_frame()
fig = fframe.gcf()
for iphase, phasename in enumerate(phasenames):
p = fig.add_subplot(1, nphases, iphase + 1)
p.set_xlabel('Event number')
p.set_ylabel('Event number')
mesh = p.pcolormesh(coefssum[iphase])
cb = fig.colorbar(mesh, ax=p)
cb.set_label('Max correlation coefficient')
if self.save:
fig.savefig(self.output_filename(dir='correlation.pdf'))
fig.canvas.draw()
# setup and solve linear system
data = []
rows = []
weights = []
for iphase in range(nphases):
for istation in range(nstations):
for ia in range(nevents):
for ib in range(ia + 1, nevents):
k = iphase, istation, ia, ib
w = coefs[k]
if not num.isfinite(tshifts[k]) \
or not num.isfinite(w) or w < self.min_corr:
continue
row = num.zeros(nevents * 4)
row[ia * 4:ia * 4 + 3] = g[iphase, istation]
row[ia * 4 + 3] = -1.0
row[ib * 4:ib * 4 + 3] = -g[iphase, istation]
row[ib * 4 + 3] = 1.0
weights.append(w)
rows.append(row)
data.append(tshifts[iphase, istation, ia, ib])
nsamp = len(data)
for i in range(4):
row = num.zeros(nevents * 4)
row[i::4] = 1.
rows.append(row)
data.append(0.0)
if self.fix_depth:
for ievent in range(nevents):
row = num.zeros(nevents * 4)
row[ievent * 4 + 2] = 1.0
rows.append(row)
data.append(0.0)
a = num.array(rows, dtype=num.float)
d = num.array(data, dtype=num.float)
w = num.array(weights, dtype=num.float)
if self.weighting == 'equal':
w[:nsamp] = 1.0
elif self.weighting == 'linear':
pass
elif self.weighting == 'quadratic':
w[:nsamp] = w[:nsamp]**2
a[:nsamp, :] *= w[:, num.newaxis]
d[:nsamp] *= w[:nsamp]
x, residuals, rank, singular = num.linalg.lstsq(a, d)
x0 = num.zeros(nevents * 4)
x0[3::4] = tevents_corr
mean_abs_residual0 = num.mean(
num.abs((num.dot(a[:nsamp], x0) - d[:nsamp]) / w[:nsamp]))
mean_abs_residual = num.mean(
num.abs((num.dot(a[:nsamp], x) - d[:nsamp]) / w[:nsamp]))
print(mean_abs_residual0, mean_abs_residual)
# distorted solutions
npermutations = 100
noiseamount = mean_abs_residual
xdistorteds = []
for i in range(npermutations):
dnoisy = d.copy()
dnoisy[:nsamp] += num.random.normal(
size=nsamp) * noiseamount * w[:nsamp]
xdistorted, residuals, rank, singular = num.linalg.lstsq(a, dnoisy)
xdistorteds.append(xdistorted)
mean_abs_residual = num.mean(
num.abs(num.dot(a, xdistorted)[:nsamp] - dnoisy[:nsamp]))
tmean = num.mean([e.time for e in events])
north = x[0::4]
east = x[1::4]
down = x[2::4]
etime = x[3::4] + tmean
def plot_range(x):
mi, ma = num.percentile(x, [10., 90.])
ext = (ma - mi) / 5.
mi -= ext
ma += ext
return mi, ma
lat, lon = orthodrome.ne_to_latlon(master.lat, master.lon, north, east)
events_out = []
for ievent, event in enumerate(events):
event_out = model.Event(time=etime[ievent],
lat=lat[ievent],
lon=lon[ievent],
depth=down[ievent] + master_depth,
name=event.name)
mark = EventMarker(event_out, kind=4)
self.add_marker(mark)
events_out.append(event_out)
model.Event.dump_catalog(events_out, 'events.relocated.txt')
# plot results
ned_orig = []
for event in events:
n, e = orthodrome.latlon_to_ne(master, event)
d = event.depth
ned_orig.append((n, e, d))
ned_orig = num.array(ned_orig)
ned_orig[:, 0] -= num.mean(ned_orig[:, 0])
ned_orig[:, 1] -= num.mean(ned_orig[:, 1])
ned_orig[:, 2] -= num.mean(ned_orig[:, 2])
north0, east0, down0 = ned_orig.T
north2, east2, down2, time2 = num.hstack(xdistorteds).reshape(
(-1, 4)).T
fframe = self.figure_frame()
fig = fframe.gcf()
color_sym = (0.1, 0.1, 0.0)
color_scat = (0.3, 0.5, 1.0, 0.2)
d = u'\u0394 '
if not self.fix_depth:
p = fig.add_subplot(2, 2, 1, aspect=1.0)
else:
p = fig.add_subplot(1, 1, 1, aspect=1.0)
mi_north, ma_north = plot_range(north)
mi_east, ma_east = plot_range(east)
mi_down, ma_down = plot_range(down)
p.set_xlabel(d + 'East [km]')
p.set_ylabel(d + 'North [km]')
p.plot(east2 / km, north2 / km, '.', color=color_scat, markersize=2)
p.plot(east / km, north / km, '+', color=color_sym)
p.plot(east0 / km, north0 / km, 'x', color=color_sym)
p0 = p
for i, ev in enumerate(events):
p.text(east[i] / km, north[i] / km, ev.name, clip_on=True)
if not self.fix_depth:
p = fig.add_subplot(2, 2, 2, sharey=p0, aspect=1.0)
p.set_xlabel(d + 'Depth [km]')
p.set_ylabel(d + 'North [km]')
p.plot(down2 / km,
north2 / km,
'.',
color=color_scat,
markersize=2)
p.plot(down / km, north / km, '+', color=color_sym)
for i, ev in enumerate(events):
p.text(down[i] / km, north[i] / km, ev.name, clip_on=True)
p1 = p
p = fig.add_subplot(2, 2, 3, sharex=p0, aspect=1.0)
p.set_xlabel(d + 'East [km]')
p.set_ylabel(d + 'Depth [km]')
p.plot(east2 / km, down2 / km, '.', color=color_scat, markersize=2)
p.plot(east / km, down / km, '+', color=color_sym)
for i, ev in enumerate(events):
p.text(east[i] / km, down[i] / km, ev.name, clip_on=True)
p.invert_yaxis()
p2 = p
p0.set_xlim(mi_east / km, ma_east / km)
p0.set_ylim(mi_north / km, ma_north / km)
if not self.fix_depth:
p1.set_xlim(mi_down / km, ma_down / km)
p2.set_ylim(mi_down / km, ma_down / km)
if self.save:
fig.savefig(self.output_filename(dir='locations.pdf'))
fig.canvas.draw()
def process(self, event, timing, bazi=None, slow=None, restitute=False, *args, **kwargs):
'''
:param timing: CakeTiming. Uses the definition without the offset.
:param fn_dump_center: filename to where center stations shall be dumped
:param fn_beam: filename of beam trace
:param model: earthmodel to use (optional)
:param earthmodel to use (optional)
:param network: network code (optional)
:param station: station code (optional)
'''
logger.debug('start beam forming')
stations = self.stations
network_code = kwargs.get('responses', None)
network_code = kwargs.get('network', '')
station_code = kwargs.get('station', 'STK')
c_station_id = (network_code, station_code)
lat_c, lon_c, z_c = self.c_lat_lon_z
self.station_c = Station(lat=float(lat_c),
lon=float(lon_c),
elevation=float(z_c),
depth=0.,
name='Array Center',
network=c_station_id[0],
station=c_station_id[1][:5])
fn_dump_center = kwargs.get('fn_dump_center', 'array_center.pf')
fn_beam = kwargs.get('fn_beam', 'beam.mseed')
if event:
mod = cake.load_model(crust2_profile=(event.lat, event.lon))
dist = ortho.distance_accurate50m(event, self.station_c)
ray = timing.t(mod, (event.depth, dist), get_ray=True)
if ray is None:
logger.error('None of defined phases available at beam station:\n %s' % self.station_c)
return
else:
b = ortho.azimuth(self.station_c, event)
if b>=0.:
self.bazi = b
elif b<0.:
self.bazi = 360.+b
self.slow = ray.p/(cake.r2d*cake.d2m)
else:
self.bazi = bazi
self.slow = slow
logger.info('stacking %s with slowness %1.4f s/km at back azimut %1.1f '
'degrees' %('.'.join(c_station_id), self.slow*cake.km, self.bazi))
lat0 = num.array([lat_c]*len(stations))
lon0 = num.array([lon_c]*len(stations))
lats = num.array([s.lat for s in stations])
lons = num.array([s.lon for s in stations])
ns, es = ortho.latlon_to_ne_numpy(lat0, lon0, lats, lons)
theta = num.float(self.bazi*num.pi/180.)
R = num.array([[num.cos(theta), -num.sin(theta)],
[num.sin(theta), num.cos(theta)]])
distances = R.dot(num.vstack((es, ns)))[1]
channels = set()
self.stacked = {}
num_stacked = {}
self.t_shifts = {}
self.shifted_traces = []
taperer = trace.CosFader(xfrac=0.05)
if self.diff_dt_treat=='downsample':
self.traces.sort(key=lambda x: x.deltat)
elif self.diff_dt_treat=='oversample':
dts = [t.deltat for t in self.traces]
for tr in self.traces:
tr.resample(min(dts))
for tr in self.traces:
if tr.nslc_id[:2] == c_station_id:
continue
tr = tr.copy(data=True)
tr.ydata = tr.ydata.astype(num.float64) - tr.ydata.mean(dtype=num.float64)
tr.taper(taperer)
try:
stack_trace = self.stacked[tr.channel]
num_stacked[tr.channel] += 1
except KeyError:
stack_trace = tr.copy(data=True)
stack_trace.set_ydata(num.zeros(
len(stack_trace.get_ydata())))
stack_trace.set_codes(network=c_station_id[0],
station=c_station_id[1],
location='',
channel=tr.channel)
self.stacked[tr.channel] = stack_trace
channels.add(tr.channel)
num_stacked[tr.channel] = 1
nslc_id = tr.nslc_id
try:
stats = filter(lambda x: util.match_nslc(
'%s.%s.%s.*' % x.nsl(), nslc_id), stations)
stat = stats[0]
except IndexError:
break
i = stations.index(stat)
d = distances[i]
t_shift = d*self.slow
tr.shift(t_shift)
#stat = viewer.get_station(tr.nslc_id[:2])
self.t_shifts[tr.nslc_id[:2]] = t_shift
if self.normalize_std:
tr.ydata = tr.ydata/tr.ydata.std()
if num.abs(tr.deltat-stack_trace.deltat)>0.000001:
if self.diff_dt_treat=='downsample':
stack_trace.downsample_to(tr.deltat)
elif self.diff_dt_treat=='upsample':
raise Exception('something went wrong with the upsampling, previously')
stack_trace.add(tr)
tr.set_station('%s_s' % tr.station)
self.shifted_traces.append(tr)
if self.post_normalize:
for ch, tr in self.stacked.items():
tr.set_ydata(tr.get_ydata()/num_stacked[ch])
#for ch, tr in self.stacked.items():
# if num_stacked[ch]>1:
# self.add_trace(tr)
self.save_station(fn_dump_center)
self.checked_nslc([stack_trace])
self.save(stack_trace, fn_beam)
position=(0., 0.),
size=2.0,
color_t=(0.7, 0.4, 0.4),
projection=projection,
size_units='data')
for rlat, rlon in rlatlons:
distance = orthodrome.distance_accurate50m(slat, slon, rlat, rlon)
rays = mod.arrivals(
phases=cake.PhaseDef('P'),
zstart=sdepth, zstop=rdepth, distances=[distance*cake.m2d])
if not rays:
continue
takeoff = rays[0].takeoff_angle()
azi = orthodrome.azimuth(slat, slon, rlat, rlon)
# to spherical coordinates, r, theta, phi in radians
rtp = num.array([[1., num.deg2rad(takeoff), num.deg2rad(90.-azi)]])
# to 3D coordinates (x, y, z)
points = beachball.numpy_rtp2xyz(rtp)
# project to 2D with same projection as used in beachball
x, y = beachball.project(points, projection=projection).T
axes.plot(x, y, '+', ms=10., mew=2.0, mec='black', mfc='none')
fig.savefig('beachball-example04.png')
