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
def call(self):
self.cleanup()
c_station_id = ('_', 'STK')
if self.unit == 's/deg':
slow_factor = 1. / onedeg
elif self.unit == 's/km':
slow_factor = 1. / 1000.
slow = self.slow * slow_factor
if self.stacked_traces is not None:
self.add_traces(self.stacked_traces)
viewer = self.get_viewer()
if self.station_c:
viewer.stations.pop(c_station_id)
stations = self.get_stations()
if len(stations) == 0:
self.fail('No station meta information found')
traces = list(self.chopper_selected_traces(fallback=True))
traces = [tr for trs in traces for tr in trs]
visible_nslcs = [tr.nslc_id for tr in traces]
stations = [
x for x in stations
if util.match_nslcs("%s.%s.%s.*" % x.nsl(), visible_nslcs)
]
if not self.lat_c or not self.lon_c or not self.z_c:
self.lat_c, self.lon_c, self.z_c = self.center_lat_lon(stations)
self.set_parameter('lat_c', self.lat_c)
self.set_parameter('lon_c', self.lon_c)
self.station_c = Station(lat=float(self.lat_c),
lon=float(self.lon_c),
elevation=float(self.z_c),
depth=0.,
name='Array Center',
network=c_station_id[0],
station=c_station_id[1])
viewer.add_stations([self.station_c])
lat0 = num.array([self.lat_c] * len(stations))
lon0 = num.array([self.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 = {}
shifted_traces = []
taperer = trace.CosFader(xfrac=0.05)
if self.diff_dt_treat == 'downsample':
traces.sort(key=lambda x: x.deltat)
elif self.diff_dt_treat == 'oversample':
dts = [t.deltat for t in traces]
for tr in traces:
tr.resample(min(dts))
for tr in traces:
if tr.nslc_id[:2] == c_station_id:
continue
tr = tr.copy(data=True)
tr.ydata = tr.ydata.astype(num.float64)
tr.ydata -= 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 = [
x for x in stations
if util.match_nslc('%s.%s.%s.*' % x.nsl(), nslc_id)
]
stat = stats[0]
except IndexError:
break
i = stations.index(stat)
d = distances[i]
t_shift = d * slow
tr.shift(t_shift)
stat = viewer.get_station(tr.nslc_id[:2])
self.t_shifts[stat] = 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':
print(
'something went wrong with the upsampling, previously')
stack_trace.add(tr)
if self.add_shifted:
tr.set_station('%s_s' % tr.station)
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.cleanup()
for ch, tr in self.stacked.items():
if num_stacked[ch] > 1:
self.add_trace(tr)
if self.add_shifted:
self.add_traces(shifted_traces)