def test_triggerOnset(self):
"""
Test trigger onset function
"""
on_of = np.array([[6.0, 31], [69, 94], [131, 181], [215, 265],
[278, 315], [480, 505], [543, 568], [605, 631]])
cft = np.concatenate((np.sin(np.arange(0, 5 * np.pi, 0.1)) + 1,
np.sin(np.arange(0, 5 * np.pi, 0.1)) + 2.1,
np.sin(np.arange(0, 5 * np.pi, 0.1)) + 0.4,
np.sin(np.arange(0, 5 * np.pi, 0.1)) + 1))
picks = triggerOnset(cft, 1.5, 1.0, max_len=50)
np.testing.assert_array_equal(picks, on_of)
# check that max_len_delete drops the picks
picks_del = triggerOnset(cft, 1.5, 1.0, max_len=50,
max_len_delete=True)
np.testing.assert_array_equal(picks_del, on_of[np.array([0, 1, 5, 6])])
#
# set True for visual understanding the tests
if False: # pragma: no cover
import matplotlib.pyplot as plt
plt.plot(cft)
plt.hlines([1.5, 1.0], 0, len(cft))
on_of = np.array(on_of)
plt.vlines(picks[:, 0], 1.0, 2.0, color='g', linewidth=2,
label="ON max_len")
plt.vlines(picks[:, 1], 0.5, 1.5, color='r', linewidth=2,
label="OF max_len")
plt.vlines(picks_del[:, 0] + 2, 1.0, 2.0, color='y', linewidth=2,
label="ON max_len_delete")
plt.vlines(picks_del[:, 1] + 2, 0.5, 1.5, color='b', linewidth=2,
label="OF max_len_delete")
plt.legend()
plt.show()
def trig(self,slta):
#get values for sta and lta
tas = slta.split(' ')
ON = eval(tas[2])
OFF = eval(tas[3])
for i in range(len(self)):
pic = triggerOnset(self[i].data, ON, OFF)
ooo = str()
for j in range(len(pic)):
ooo = ooo + str(pic[j])
self[i].stats['trigger_on'] = ON
self[i].stats['trigger_off'] = OFF
self[i].stats['picks'] = ooo
return self
print "Cannot process station %s, no RESP file given" % tr.stats.station
continue
# Cannot process a whole day file, split it in smaller junks
overlap = s2p(30.0, tr)
olap = overlap
samp = 0
df = tr.stats.sampling_rate
if trId(tr.stats)[1] != last_id or tr.stats.starttime - last_endtime > 1.0 / df:
data_buf = np.array([], dtype='float64')
olap = 0
while samp < tr.stats.npts:
data = tr.data[samp:samp + nfft - olap].astype('float64')
data = np.concatenate((data_buf, data))
data = detrend(data)
# Correct for frequency response of instrument
data = seisSim(data, tr.stats.sampling_rate, paz, inst_sim=inst)
data /= (paz['sensitivity'] / 1e9) #V/nm/s correct for overall sensitivity
data = recStalta(data, s2p(2.5, tr), s2p(10.0, tr))
picked_values = triggerOnset(data, 3.0, 0.5, max_len=overlap)
#
for i, j in picked_values:
begin = tr.stats.starttime + float(i + samp - olap) / df
end = tr.stats.starttime + float(j + samp - olap) / df
f.write("%s,%s,%s\n" % (str(begin), str(end), tr.stats.station))
olap = overlap # only needed for first time in loop
samp += nfft - overlap
data_buf = data[-overlap:]
print '.', # Progress Bar
last_endtime, last_id = trId(tr.stats)
f.close()
olap = overlap
samp = 0
df = tr.stats.sampling_rate
if trId(
tr.stats
)[1] != last_id or tr.stats.starttime - last_endtime > 1.0 / df:
data_buf = np.array([], dtype='float64')
olap = 0
while samp < tr.stats.npts:
data = tr.data[samp:samp + nfft - olap].astype('float64')
data = np.concatenate((data_buf, data))
data = detrend(data)
# Correct for frequency response of instrument
data = seisSim(data, tr.stats.sampling_rate, paz, inst_sim=inst)
data /= (paz['sensitivity'] / 1e9
) #V/nm/s correct for overall sensitivity
data = recStalta(data, s2p(2.5, tr), s2p(10.0, tr))
picked_values = triggerOnset(data, 3.0, 0.5, max_len=overlap)
#
for i, j in picked_values:
begin = tr.stats.starttime + float(i + samp - olap) / df
end = tr.stats.starttime + float(j + samp - olap) / df
f.write("%s,%s,%s\n" %
(str(begin), str(end), tr.stats.station))
olap = overlap # only needed for first time in loop
samp += nfft - overlap
data_buf = data[-overlap:]
print '.', # Progress Bar
last_endtime, last_id = trId(tr.stats)
f.close()
olap = 0
while samp < tr.stats.npts:
data = tr.data[samp:samp + nfft - olap].astype('float64')
data = np.concatenate((data_buf, data))
data = detrend(data)
# Correct for frequency response of instrument
data = seisSim(data,
df,
paz_remove=tr.stats.paz,
paz_simulate=inst,
remove_sensitivity=True)
# XXX is removed in seisSim... ?!
# XXX data /= (paz['sensitivity'] / 1e9) #V/nm/s correct for overall sensitivity
data = bandpass(data, LOW, HIGH, df)
data = recStalta(data, s2p(STA, tr), s2p(LTA, tr))
picked_values = triggerOnset(data, ON, OFF, max_len=overlap)
#
for i, j in picked_values:
begin = tr.stats.starttime + float(i + samp - olap) / df
end = tr.stats.starttime + float(j + samp - olap) / df
trigger_list.append(
(begin.timestamp, end.timestamp, tr.stats.station))
olap = overlap # only needed for first time in loop
samp += nfft - overlap
data_buf = data[-overlap:]
last_endtime, last_id = trId(tr.stats)
###############################################################################
# start of coincidence part
###############################################################################
trigger_list.sort()
tr.data = detrend(tr.data)
st.simulate(paz_remove="self", paz_simulate=cornFreq2Paz(1.0), remove_sensitivity=False)
st.sort()
st_trigger = st.copy()
st_trigger.filter("bandpass", freqmin=PAR.LOW, freqmax=PAR.HIGH, corners=1, zerophase=True)
st.trim(T1, T2)
st_trigger.trim(T1, T2)
st_trigger.trigger("recstalta", sta=PAR.STA, lta=PAR.LTA)
summary.append(str(st))
# do the triggering
trigger_list = []
for tr in st_trigger:
tr.stats.channel = "recstalta"
max_len = PAR.MAXLEN * tr.stats.sampling_rate
trigger_sample_list = triggerOnset(tr.data, PAR.ON, PAR.OFF, max_len=max_len)
for on, off in trigger_sample_list:
begin = tr.stats.starttime + float(on) / tr.stats.sampling_rate
end = tr.stats.starttime + float(off) / tr.stats.sampling_rate
trigger_list.append((begin.timestamp, end.timestamp, tr.stats.station))
trigger_list.sort()
# merge waveform and trigger stream for plotting
# the normalizations are done because the triggers have a completely different
# scale and would not be visible in the plot otherwise...
st.filter("bandpass", freqmin=1.0, freqmax=20.0, corners=1, zerophase=True)
st.normalize(global_max=False)
st_trigger.normalize(global_max=True)
st.extend(st_trigger)
# coincidence part, work through sorted trigger list...
samp = 0
df = tr.stats.sampling_rate
if trId(tr.stats)[1] != last_id or tr.stats.starttime - last_endtime > 1.0 / df:
data_buf = np.array([], dtype='float64')
olap = 0
while samp < tr.stats.npts:
data = tr.data[samp:samp + nfft - olap].astype('float64')
data = np.concatenate((data_buf, data))
data = detrend(data)
# Correct for frequency response of instrument
data = seisSim(data, df, paz_remove=tr.stats.paz, paz_simulate=inst, remove_sensitivity=True)
# XXX is removed in seisSim... ?!
# XXX data /= (paz['sensitivity'] / 1e9) #V/nm/s correct for overall sensitivity
data = bandpass(data, LOW, HIGH, df)
data = recStalta(data, s2p(STA, tr), s2p(LTA, tr))
picked_values = triggerOnset(data, ON, OFF, max_len=overlap)
#
for i, j in picked_values:
begin = tr.stats.starttime + float(i + samp - olap) / df
end = tr.stats.starttime + float(j + samp - olap) / df
trigger_list.append((begin.timestamp, end.timestamp, tr.stats.station))
olap = overlap # only needed for first time in loop
samp += nfft - overlap
data_buf = data[-overlap:]
last_endtime, last_id = trId(tr.stats)
###############################################################################
# start of coincidence part
###############################################################################
trigger_list.sort()
#print [(UTCDateTime(i[0]).isoformat(), UTCDateTime(i[1]).isoformat(), i[2]) for i in trigger_list]
freqmin=PAR.LOW,
freqmax=PAR.HIGH,
corners=1,
zerophase=True)
st.trim(T1, T2)
st_trigger.trim(T1, T2)
st_trigger.trigger("recstalta", sta=PAR.STA, lta=PAR.LTA)
summary.append(str(st))
# do the triggering
trigger_list = []
for tr in st_trigger:
tr.stats.channel = "recstalta"
max_len = PAR.MAXLEN * tr.stats.sampling_rate
trigger_sample_list = triggerOnset(tr.data,
PAR.ON,
PAR.OFF,
max_len=max_len)
for on, off in trigger_sample_list:
begin = tr.stats.starttime + float(on) / tr.stats.sampling_rate
end = tr.stats.starttime + float(off) / tr.stats.sampling_rate
trigger_list.append(
(begin.timestamp, end.timestamp, tr.stats.station))
trigger_list.sort()
# merge waveform and trigger stream for plotting
# the normalizations are done because the triggers have a completely different
# scale and would not be visible in the plot otherwise...
st.filter("bandpass", freqmin=1.0, freqmax=20.0, corners=1, zerophase=True)
st.normalize(global_max=False)
st_trigger.normalize(global_max=True)
st.extend(st_trigger)