def _off_test_synthetic(self):
from pyrocko import gf
km = 1000.
nstations = 10
edepth = 5 * km
store_id = 'crust2_d0'
swin = 2.
lwin = 9. * swin
ks = 1.0
kl = 1.0
kd = 3.0
engine = gf.get_engine()
snorths = (num.random.random(nstations) - 1.0) * 50 * km
seasts = (num.random.random(nstations) - 1.0) * 50 * km
targets = []
for istation, (snorths, seasts) in enumerate(zip(snorths, seasts)):
targets.append(
gf.Target(quantity='displacement',
codes=('', 's%03i' % istation, '', 'Z'),
north_shift=float(snorths),
east_shift=float(seasts),
store_id=store_id,
interpolation='multilinear'))
source = gf.DCSource(north_shift=50 * km,
east_shift=50 * km,
depth=edepth)
store = engine.get_store(store_id)
response = engine.process(source, targets)
trs = []
station_traces = defaultdict(list)
station_targets = defaultdict(list)
for source, target, tr in response.iter_results():
tp = store.t('any_P', source, target)
t = tp - 5 * tr.deltat + num.arange(11) * tr.deltat
if False:
gauss = trace.Trace(tmin=t[0],
deltat=tr.deltat,
ydata=num.exp(-((t - tp)**2) /
((2 * tr.deltat)**2)))
tr.ydata[:] = 0.0
tr.add(gauss)
trs.append(tr)
station_traces[target.codes[:3]].append(tr)
station_targets[target.codes[:3]].append(target)
station_stalta_traces = {}
for nsl, traces in station_traces.items():
etr = None
for tr in traces:
sqr_tr = tr.copy(data=False)
sqr_tr.ydata = tr.ydata**2
if etr is None:
etr = sqr_tr
else:
etr += sqr_tr
autopick.recursive_stalta(swin, lwin, ks, kl, kd, etr)
etr.set_codes(channel='C')
station_stalta_traces[nsl] = etr
trace.snuffle(trs + list(station_stalta_traces.values()))
deltat = trs[0].deltat
nnorth = 50
neast = 50
size = 200 * km
north = num.linspace(-size, size, nnorth)
north2 = num.repeat(north, neast)
east = num.linspace(-size, size, neast)
east2 = num.tile(east, nnorth)
depth = 5 * km
def tcal(target, i):
try:
return store.t(
'any_P',
gf.Location(north_shift=north2[i],
east_shift=east2[i],
depth=depth), target)
except gf.OutOfBounds:
return 0.0
nsls = sorted(station_stalta_traces.keys())
tts = num.fromiter((tcal(station_targets[nsl][0], i)
for i in range(nnorth * neast) for nsl in nsls),
dtype=num.float)
arrays = [
station_stalta_traces[nsl].ydata.astype(num.float) for nsl in nsls
]
offsets = num.array([
int(round(station_stalta_traces[nsl].tmin / deltat))
for nsl in nsls
],
dtype=num.int32)
shifts = -num.array([int(round(tt / deltat)) for tt in tts],
dtype=num.int32).reshape(nnorth * neast, nstations)
weights = num.ones((nnorth * neast, nstations))
print(shifts[25 * neast + 25] * deltat)
print(offsets.dtype, shifts.dtype, weights.dtype)
print('stack start')
mat, ioff = parstack(arrays, offsets, shifts, weights, 1)
print('stack stop')
mat = num.reshape(mat, (nnorth, neast))
from matplotlib import pyplot as plt
fig = plt.figure()
axes = fig.add_subplot(1, 1, 1, aspect=1.0)
axes.contourf(east / km, north / km, mat)
axes.plot(
g(targets, 'east_shift') / km,
g(targets, 'north_shift') / km, '^')
axes.plot(source.east_shift / km, source.north_shift / km, 'o')
plt.show()
def off_test_synthetic(self):
from pyrocko import gf
km = 1000.
nstations = 10
edepth = 5*km
store_id = 'crust2_d0'
swin = 2.
lwin = 9.*swin
ks = 1.0
kl = 1.0
kd = 3.0
engine = gf.get_engine()
snorths = (num.random.random(nstations)-1.0) * 50*km
seasts = (num.random.random(nstations)-1.0) * 50*km
targets = []
for istation, (snorths, seasts) in enumerate(zip(snorths, seasts)):
targets.append(
gf.Target(
quantity='displacement',
codes=('', 's%03i' % istation, '', 'Z'),
north_shift=float(snorths),
east_shift=float(seasts),
store_id=store_id,
interpolation='multilinear'))
source = gf.DCSource(
north_shift=50*km,
east_shift=50*km,
depth=edepth)
store = engine.get_store(store_id)
response = engine.process(source, targets)
trs = []
station_traces = defaultdict(list)
station_targets = defaultdict(list)
for source, target, tr in response.iter_results():
tp = store.t('any_P', source, target)
t = tp - 5 * tr.deltat + num.arange(11) * tr.deltat
if False:
gauss = trace.Trace(
tmin=t[0],
deltat=tr.deltat,
ydata=num.exp(-((t-tp)**2)/((2*tr.deltat)**2)))
tr.ydata[:] = 0.0
tr.add(gauss)
trs.append(tr)
station_traces[target.codes[:3]].append(tr)
station_targets[target.codes[:3]].append(target)
station_stalta_traces = {}
for nsl, traces in station_traces.iteritems():
etr = None
for tr in traces:
sqr_tr = tr.copy(data=False)
sqr_tr.ydata = tr.ydata**2
if etr is None:
etr = sqr_tr
else:
etr += sqr_tr
autopick.recursive_stalta(swin, lwin, ks, kl, kd, etr)
etr.set_codes(channel='C')
station_stalta_traces[nsl] = etr
trace.snuffle(trs + station_stalta_traces.values())
deltat = trs[0].deltat
nnorth = 50
neast = 50
size = 400*km
north = num.linspace(-size/2., size/2., nnorth)
north2 = num.repeat(north, neast)
east = num.linspace(-size/2., size/2., neast)
east2 = num.tile(east, nnorth)
depth = 5*km
def tcal(target, i):
try:
return store.t(
'any_P',
gf.Location(
north_shift=north2[i],
east_shift=east2[i],
depth=depth),
target)
except gf.OutOfBounds:
return 0.0
nsls = sorted(station_stalta_traces.keys())
tts = num.fromiter((tcal(station_targets[nsl][0], i)
for i in xrange(nnorth*neast)
for nsl in nsls), dtype=num.float)
arrays = [
station_stalta_traces[nsl].ydata.astype(num.float) for nsl in nsls]
offsets = num.array(
[int(round(station_stalta_traces[nsl].tmin / deltat))
for nsl in nsls], dtype=num.int32)
shifts = -num.array(
[int(round(tt / deltat))
for tt in tts], dtype=num.int32).reshape(nnorth*neast, nstations)
weights = num.ones((nnorth*neast, nstations))
print shifts[25*neast + 25] * deltat
print offsets.dtype, shifts.dtype, weights.dtype
print 'stack start'
mat, ioff = parstack(arrays, offsets, shifts, weights, 1)
print 'stack stop'
mat = num.reshape(mat, (nnorth, neast))
from matplotlib import pyplot as plt
fig = plt.figure()
axes = fig.add_subplot(1, 1, 1, aspect=1.0)
axes.contourf(east/km, north/km, mat)
axes.plot(
g(targets, 'east_shift')/km,
g(targets, 'north_shift')/km, '^')
axes.plot(source.east_shift/km, source.north_shift/km, 'o')
plt.show()
def stalta(self):
'''
Based on stalta by Francesco Grigoli.
'''
tnow = time.time()
pile = self.get_view().pile
if self._tlast_stalta is None:
self._tlast_stalta = tnow
return
tmin = self._tlast_stalta
tmax = tnow
self._tlast_stalta = tnow
swin, ratio = 0.07, 8
lwin = swin * ratio
tpad = lwin
ks = 1.0
kl = 1.8
kd = 0.
level = 6.0
_numMarkers = 0
self.markers = []
for traces in pile.chopper_grouped(tmin=tmin, tmax=tmax, tpad=tpad, want_incomplete=True,
gather=lambda tr: tr.nslc_id[:3]):
etr = None
nslcs = []
deltat = 0.
for trace in traces:
if deltat == 0:
deltat = trace.deltat
else:
if abs(deltat - trace.deltat) > 0.0001*deltat:
logger.error('skipping trace %s.%s.%s.%s with unexpected sampling rate' % trace.nslc_id)
continue
lowpass = 20
highpass = 1
trace.lowpass(4, lowpass)
trace.highpass(4, highpass)
trace.ydata = trace.ydata**2
trace.ydata = trace.ydata.astype(num.float32)
if etr is None:
etr = trace
else:
etr.add(trace)
nslcs.append(trace.nslc_id)
if etr is not None:
autopick.recursive_stalta(swin, lwin, ks, kl, kd, etr)
etr.shift(-swin)
etr.set_codes(channel='STA/LTA')
etr.meta = { 'tabu': True }
etr.chop(etr.tmin + lwin, etr.tmax - lwin)
tpeaks, apeaks, tzeros = etr.peaks(level, swin*2., deadtime=True)
for t, a in zip(tpeaks, apeaks):
staz=nslcs[0]
mark = pile_viewer.Marker(nslcs, t,t)
print mark
self.markers.append(mark)
print 'xx'
v = self.get_view()
_numMarkers+=len(self.markers)
# emit signal, when Event was detected:
if _numMarkers>=10:
self.emit(SIGNAL('valueChanged(int)'),_numMarkers)
v.add_markers(self.markers)
def preprocess(self, trs, wmin, wmax, tpad_new, deltat_cf):
fsmooth = self.get_fsmooth()
fnormalize = self.get_fnormalize()
if self.trace_selector:
trs = self.trace_selector(trs)
if not trs:
return []
trs_filt = []
for orig_tr in trs:
tr = orig_tr.copy()
tr.highpass(4, self.fmin, demean=True)
tr.lowpass(4, self.fmax, demean=False)
tr.ydata = tr.ydata**2
trs_filt.append(tr)
trs_by_nsl = common.grouped_by(trs_filt, lambda tr: tr.nslc_id[:3])
swin = self.short_window
lwin = self.window_ratio * swin
dataset = []
for nsl in sorted(trs_by_nsl.keys()):
sumtr = None
for tr in trs_by_nsl[nsl]:
if sumtr is None:
sumtr = tr.copy()
sumtr.set_codes(channel='CF_%s' % self.name)
else:
sumtr.add(tr)
sumtr.set_ydata(sumtr.get_ydata().astype(num.float32))
autopick.recursive_stalta(swin, lwin, 1.0, 4.0, 3.0, sumtr)
sumtr.shift(-swin/2.)
normtr = sumtr.copy()
ntap_smooth = int(num.round(1./fsmooth / tr.deltat))
ntap_normalize = int(num.round(1./fnormalize / tr.deltat))
taper_smooth = num.hanning(ntap_smooth)
taper_normalize = num.hanning(ntap_normalize)
sumtr.shift(-(ntap_smooth / 2. * tr.deltat))
normtr.shift(-(ntap_normalize / 2. * tr.deltat))
sumtr.set_ydata(
fftconvolve(sumtr.get_ydata()/len(trs_by_nsl), taper_smooth))
normtr.set_ydata(
fftconvolve(
normtr.get_ydata()/len(trs_by_nsl),
taper_normalize))
normtr.set_codes(channel='normtr')
tmin = max(sumtr.tmin, normtr.tmin)
tmax = min(sumtr.tmax, normtr.tmax)
sumtr.chop(tmin, tmax)
normtr.chop(tmin, tmax)
sumtr.set_ydata(sumtr.get_ydata() / normtr.get_ydata())
downsample(sumtr, deltat_cf)
sumtr.chop(wmin - tpad_new, wmax + tpad_new)
dataset.append((nsl, sumtr))
return dataset
