def _internal_process(st,
lowcut,
highcut,
filt_order,
sampling_rate,
first_length,
stachan,
i=0):
tr = st.select(station=stachan[0], channel=stachan[1])
if len(tr) == 0:
tr = Trace(np.zeros(int(first_length * sampling_rate)))
tr.stats.station = stachan[0]
tr.stats.channel = stachan[1]
tr.stats.sampling_rate = sampling_rate
tr.stats.starttime = st[0].stats.starttime # Do this to make more
# sensible plots
Logger.warning('Padding stream with zero trace for ' + 'station ' +
stachan[0] + '.' + stachan[1])
elif len(tr) == 1:
tr = tr[0]
tr.detrend('simple')
tr = pre_processing.process(tr=tr,
lowcut=lowcut,
highcut=highcut,
filt_order=filt_order,
samp_rate=sampling_rate,
seisan_chan_names=False)
else:
raise IOError('Multiple channels for {0}.{1} in a single design '
'stream.'.format(stachan[0], stachan[1]))
return i, tr
def _get_all_motion_types(tr: obspy.Trace, motion_type: str) -> Dict:
"""Get a dict of all motion types. First detrend"""
assert motion_type == "velocity"
tr.detrend("linear")
# init copies for performing transformations
acc = tr.copy()
dis = tr.copy()
# perform integrations/differentiations
dis.integrate()
dis.detrend("linear")
acc.differentiate()
acc.detrend("linear")
return dict(displacement=dis, velocity=tr, acceleration=acc)
def test_detrend(self):
"""
Test detrend method of trace
"""
t = np.arange(10)
data = 0.1 * t + 1.
tr = Trace(data=data.copy())
tr.detrend(type='simple')
np.testing.assert_array_almost_equal(tr.data, np.zeros(10))
tr.data = data.copy()
tr.detrend(type='linear')
np.testing.assert_array_almost_equal(tr.data, np.zeros(10))
data = np.zeros(10)
data[3:7] = 1.
tr.data = data.copy()
tr.detrend(type='simple')
np.testing.assert_almost_equal(tr.data[0], 0.)
np.testing.assert_almost_equal(tr.data[-1], 0.)
tr.data = data.copy()
tr.detrend(type='linear')
np.testing.assert_almost_equal(tr.data[0], -0.4)
np.testing.assert_almost_equal(tr.data[-1], -0.4)
def test_detrend(self):
"""
Test detrend method of trace
"""
t = np.arange(10)
data = 0.1 * t + 1.
tr = Trace(data=data.copy())
tr.detrend(type='simple')
np.testing.assert_array_almost_equal(tr.data, np.zeros(10))
tr.data = data.copy()
tr.detrend(type='linear')
np.testing.assert_array_almost_equal(tr.data, np.zeros(10))
data = np.zeros(10)
data[3:7] = 1.
tr.data = data.copy()
tr.detrend(type='simple')
np.testing.assert_almost_equal(tr.data[0], 0.)
np.testing.assert_almost_equal(tr.data[-1], 0.)
tr.data = data.copy()
tr.detrend(type='linear')
np.testing.assert_almost_equal(tr.data[0], -0.4)
np.testing.assert_almost_equal(tr.data[-1], -0.4)
def extract_s(taupy_model,
pickerlist,
event,
station_longitude,
station_latitude,
stn,
ste,
ba,
win_start=-50,
win_end=50,
resample_hz=20,
bp_freqmins=[0.01, 0.01, 0.5],
bp_freqmaxs=[1, 2., 5.],
margin=None,
max_amplitude=1e8,
plot_output_folder=None):
po = event.preferred_origin
if (not po): return None
atimes = []
try:
atimes = taupy_model.get_travel_times_geo(po.depthkm,
po.lat,
po.lon,
station_latitude,
station_longitude,
phase_list=('S', ))
except:
return None
# end try
if (len(atimes) == 0): return None
tat = atimes[0].time # theoretical arrival time
buffer_start = -10
buffer_end = 10
snrtr = None
try:
stn = stn.slice(po.utctime + tat + win_start + buffer_start,
po.utctime + tat + win_end + buffer_end)
stn = stn.copy()
stn.resample(resample_hz)
stn.detrend('linear')
if (ste):
ste = ste.slice(po.utctime + tat + win_start + buffer_start,
po.utctime + tat + win_end + buffer_end)
ste = ste.copy()
ste.resample(resample_hz)
ste.detrend('linear')
# end if
if (ste):
if (type(stn[0].data) == np.ndarray
and type(ste[0].data) == np.ndarray):
rc, tc = rotate_ne_rt(stn[0].data, ste[0].data, ba)
snrtr = Trace(data=tc, header=stn[0].stats)
snrtr.detrend('linear')
# end if
else:
if (type(stn[0].data) == np.ndarray):
snrtr = stn[0]
# end if
# end if
except Exception as e:
return None
# end try
if (type(snrtr.data) == np.ndarray):
if (np.max(snrtr.data) > max_amplitude): return None
pickslist = []
snrlist = []
residuallist = []
bandindex = -1
pickerindex = -1
taper_percentage = float(buffer_end) / float(win_end)
foundpicks = False
for i in range(len(bp_freqmins)):
trc = snrtr.copy()
trc.taper(max_percentage=taper_percentage, type='hann')
trc.filter('bandpass',
freqmin=bp_freqmins[i],
freqmax=bp_freqmaxs[i],
corners=4,
zerophase=True)
trc = trc.slice(po.utctime + tat + win_start,
po.utctime + tat + win_end)
for ipicker, picker in enumerate(pickerlist):
try:
scnl, picks, polarity, snr, uncert = picker.picks(trc)
for ipick, pick in enumerate(picks):
actualArrival = pick - po.utctime
residual = actualArrival - tat
if ((margin and np.fabs(residual) < margin)
or (margin == None)):
pickslist.append(pick)
plotinfo = None
if (plot_output_folder):
plotinfo = {
'eventid': event.public_id,
'origintime': po.utctime,
'mag':
event.preferred_magnitude.magnitude_value,
'net': trc.stats.network,
'sta': trc.stats.station,
'phase': 's',
'ppsnr': snr[ipick],
'pickid': ipick,
'outputfolder': plot_output_folder
}
# end if
wab = snrtr.slice(pick - 3, pick + 3)
wab_filtered = trc.slice(pick - 3, pick + 3)
scales = np.logspace(0.5, 4, 30)
cwtsnr, dom_freq, slope_ratio = compute_quality_measures(
wab, wab_filtered, scales, plotinfo)
snrlist.append(
[snr[ipick], cwtsnr, dom_freq, slope_ratio])
residuallist.append(residual)
bandindex = i
pickerindex = ipicker
foundpicks = True
# end if
# end for
except:
continue
# end try
if (foundpicks): break
# end for
if (foundpicks): break
# end for
if (len(pickslist)):
return pickslist, residuallist, \
np.array(snrlist), bandindex, pickerindex
# end if
# end if
return None
str(day),
str(tr.stats.station),
str(tr.stats.channel),
)
if os.path.exists(finpc1) and os.path.getsize(finpc1) > 0:
try:
st = read(finpc1, starttime=t1, endtime=t2)
if len(st) != 0:
st.merge(method=1, fill_value=0)
tc = st[0]
stat = tc.stats.station
chan = tc.stats.channel
tc.detrend("constant")
# 24h continuous trace starts 00 h 00 m 00.0s
trim_fill(tc, t1, t2)
tc.filter(
"bandpass",
freqmin=bandpass[0],
freqmax=bandpass[1],
zerophase=True,
)
# store detrended and filtered continuous data
# in a Stream
stream_df += Stream(traces=[tc])
except IOError:
pass
def extract_s(taupy_model,
picker,
event,
station_longitude,
station_latitude,
stn,
ste,
ba,
win_start=-50,
win_end=50,
resample_hz=20,
bp_freqmins=[0.05, 2],
bp_freqmaxs=[0.5, 5],
margin=20,
max_amplitude=1e8):
po = event.preferred_origin
if (not po): return None
atimes = []
try:
atimes = taupy_model.get_travel_times_geo(po.depthkm,
po.lat,
po.lon,
station_latitude,
station_longitude,
phase_list=('S', ))
except:
return None
# end try
if (len(atimes) == 0): return None
tat = atimes[0].time # theoretical arrival time
tr = None
try:
stn = stn.slice(po.utctime + tat + win_start,
po.utctime + tat + win_end)
stn.resample(resample_hz)
if (ste):
ste = ste.slice(po.utctime + tat + win_start,
po.utctime + tat + win_end)
ste.resample(resample_hz)
# end if
if (ste):
if (type(stn[0].data) == np.ndarray
and type(ste[0].data) == np.ndarray):
rc, tc = rotate_ne_rt(stn[0].data, ste[0].data, ba)
tr = Trace(data=tc, header=stn[0].stats)
#tr = Trace(data=np.sqrt(np.power(rc,2) + np.power(tc,2)), header=stn[0].stats)
# end if
else:
if (type(stn[0].data) == np.ndarray):
tr = stn[0]
# end if
# end if
except Exception as e:
return None
# end try
if (tr):
if (np.max(tr.data) > max_amplitude): return None
pickslist = []
snrlist = []
residuallist = []
tr.detrend('linear')
for i in range(len(bp_freqmins)):
trc = tr.copy()
trc.filter('bandpass',
freqmin=bp_freqmins[i],
freqmax=bp_freqmaxs[i],
corners=4,
zerophase=True)
try:
scnl, picks, polarity, snr, uncert = picker.picks(trc)
for ipick, pick in enumerate(picks):
actualArrival = pick - po.utctime
residual = actualArrival - tat
if (np.fabs(residual) < margin):
pickslist.append(pick)
snrlist.append(snr[ipick])
residuallist.append(residual)
#summary = fbpicker.FBSummary(picker, trc)
#summary = aicdpicker.AICDSummary(picker, trc)
#outputPath = '/home/rakib/work/pst/picking/sarr'
#outputPath = '/g/data1a/ha3/rakib/seismic/pst/tests/plots/new'
#ofn = '%s/%s.%s_%f_%d.s.png' % (outputPath, scnl, str(po.utctime), snr[0], i)
#summary.plot_picks(show=False, savefn=ofn)
# end if
# end for
except:
continue
# end try
# end for
if (len(pickslist)):
optimal_pick_idx = np.argmax(np.array(snrlist))
return pickslist[optimal_pick_idx], residuallist[optimal_pick_idx], \
snrlist[optimal_pick_idx], optimal_pick_idx
# end if
# end if
return None
def simulate_trace(
self,
max_amplitude: float = 1e-4,
width: float = 0.2,
noise: bool = False,
) -> Tuple[Trace, Event, float, float]:
""" Make a wood-anderson trace and convolve it with a response. """
from scipy.signal import ricker
# Make dummy data in meters on Wood Anderson
np.random.seed(42)
if noise:
data = np.random.randn(int(self.sampling_rate * self.length))
else:
data = np.zeros(int(self.sampling_rate * self.length))
wavelet = ricker(
int(self.sampling_rate * self.length),
a=(width * self.sampling_rate / 4))
wavelet /= wavelet.max()
wavelet *= max_amplitude
vals = sorted([(val, ind) for ind, val in enumerate(wavelet)],
key=lambda x: x[0])
period = abs(vals[0][1] - vals[1][1]) / self.sampling_rate
half_max = (wavelet.max() - wavelet.min()) / 2
data += wavelet
# Make dummy trace
tr = Trace(data=data, header=dict(
station=self.inv[0][0].code,
network=self.inv[0].code,
location=self.inv[0][0][0].location_code,
channel=self.inv[0][0][0].code,
sampling_rate=self.sampling_rate,
starttime=UTCDateTime(2020, 1, 1)))
tr = tr.detrend()
# Remove Wood-Anderson response and simulate seismometer
resp = self.inv[0][0][0].response
paz = resp.get_paz()
paz = {
'poles': paz.poles,
'zeros': paz.zeros,
'gain': paz.normalization_factor,
'sensitivity': resp.instrument_sensitivity.value,
}
tr = tr.simulate(
paz_remove=PAZ_WA, paz_simulate=paz)
# Make an event
mid_point = tr.stats.starttime + 0.5 * (
tr.stats.endtime - tr.stats.starttime)
event = Event(picks=[
Pick(phase_hint="P",
time=mid_point - 3.0,
waveform_id=WaveformStreamID(
network_code=tr.stats.network,
station_code=tr.stats.station,
location_code=tr.stats.location,
channel_code=tr.stats.channel)),
Pick(phase_hint="S", time=mid_point,
waveform_id=WaveformStreamID(
network_code=tr.stats.network,
station_code=tr.stats.station,
location_code=tr.stats.location,
channel_code=tr.stats.channel))])
return tr, event, period, half_max
