def findEvents(condir='ContinousWaveForms',stakey='StationKey.csv',chan='Z',
trigDir='Trigs',startbuff=25,endbuff=200,trigBuff=2000):
stations=pd.read_csv(stakey)
if not os.path.isdir(trigDir):
os.makedirs(trigDir)
years,juldays=getConRange(stations,condir)
for a in range(len(years)):
for b in juldays[a]:
STfull=makeStream(stations,years[a],b,condir)
STfull.sort()
st=STfull.copy()
st=st.select(channel='*Z')
trig = coincidenceTrigger("recstalta", 5, 1.0, st, 7, sta=0.5, lta=15,details=True,trigger_off_extension=20)
trig=trimTrig(trig,trigBuff)
tt=[0]*len(trig)
for c in range(len(tt)):
if trig[c] != None:
try:
tt[c]=STfull.slice(starttime=trig[c]['time']-startbuff,endtime=trig[c]['time']+trig[c]['duration']+endbuff)
except:
global de,t,C
de,t,C=trig,tt,c
sys.exit(1)
saveTrace(tt[c],trigDir,trig[c]['time'])
mutt = []
if st:
# preprocessing, backup original data for plotting at end
st.merge(0)
st.detrend("linear")
for tr in st:
tr.data = tr.data * cosTaper(len(tr), 0.01)
#st.simulate(paz_remove="self", paz_simulate=cornFreq2Paz(1.0), remove_sensitivity=False)
st.sort()
st.filter("bandpass", freqmin=PAR.LOW, freqmax=PAR.HIGH, corners=1, zerophase=True)
st.trim(T1, T2)
st_trigger = st.copy()
st.normalize(global_max=False)
# do the triggering
trig = coincidenceTrigger("recstalta", PAR.ON, PAR.OFF, st_trigger,
thr_coincidence_sum=PAR.MIN_STATIONS,
max_trigger_length=PAR.MAXLEN, trigger_off_extension=PAR.ALLOWANCE,
details=True, sta=PAR.STA, lta=PAR.LTA)
for t in trig:
info = "%s %ss %s %s" % (t['time'].strftime("%Y-%m-%dT%H:%M:%S"), ("%.1f" % t['duration']).rjust(4), ("%i" % t['cft_peak_wmean']).rjust(3), "-".join(t['stations']))
summary.append(info)
tmp = st.slice(t['time'] - 1, t['time'] + t['duration'])
outfilename = "%s/%s_%.1f_%i_%s-%s_%s.png" % (PLOTDIR, t['time'].strftime("%Y-%m-%dT%H:%M:%S"), t['duration'], t['cft_peak_wmean'], len(t['stations']), num_stations, "-".join(t['stations']))
tmp.plot(outfile=outfilename)
mutt += ("-a", outfilename)
summary.append("#" * 79)
summary = "\n".join(summary)
summary += "\n" + "\n".join(("%s=%s" % (k, v) for k, v in PAR.items()))
#print summary
open(SUMMARY, "at").write(summary + "\n")
def test_coincidenceTriggerWithSimilarityChecking(self):
"""
Test network coincidence trigger with cross correlation similarity
checking of given event templates.
"""
st = Stream()
files = [
"BW.UH1._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH2._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH3._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH3._.SHN.D.2010.147.cut.slist.gz",
"BW.UH3._.SHE.D.2010.147.cut.slist.gz",
"BW.UH4._.EHZ.D.2010.147.cut.slist.gz"
]
for filename in files:
filename = os.path.join(self.path, filename)
st += read(filename)
# some prefiltering used for UH network
st.filter('bandpass', freqmin=10, freqmax=20)
# set up template event streams
times = ["2010-05-27T16:24:33.095000", "2010-05-27T16:27:30.370000"]
templ = {}
for t in times:
t = UTCDateTime(t)
st_ = st.select(station="UH3").slice(t, t + 2.5).copy()
templ.setdefault("UH3", []).append(st_)
times = ["2010-05-27T16:27:30.574999"]
for t in times:
t = UTCDateTime(t)
st_ = st.select(station="UH1").slice(t, t + 2.5).copy()
templ.setdefault("UH1", []).append(st_)
trace_ids = {
"BW.UH1..SHZ": 1,
"BW.UH2..SHZ": 1,
"BW.UH3..SHZ": 1,
"BW.UH4..EHZ": 1
}
similarity_thresholds = {"UH1": 0.8, "UH3": 0.7}
trig = coincidenceTrigger("classicstalta",
5,
1,
st.copy(),
4,
sta=0.5,
lta=10,
trace_ids=trace_ids,
event_templates=templ,
similarity_threshold=similarity_thresholds)
# check floats in resulting dictionary separately
self.assertAlmostEqual(trig[0].pop('duration'), 3.9600000381469727)
self.assertAlmostEqual(trig[1].pop('duration'), 1.9900000095367432)
self.assertAlmostEqual(trig[2].pop('duration'), 1.9200000762939453)
self.assertAlmostEqual(trig[3].pop('duration'), 3.9200000762939453)
self.assertAlmostEqual(trig[0]['similarity'].pop('UH1'), 0.94149447384)
self.assertAlmostEqual(trig[0]['similarity'].pop('UH3'), 1)
self.assertAlmostEqual(trig[1]['similarity'].pop('UH1'), 0.65228204570)
self.assertAlmostEqual(trig[1]['similarity'].pop('UH3'), 0.72679293429)
self.assertAlmostEqual(trig[2]['similarity'].pop('UH1'), 0.89404458774)
self.assertAlmostEqual(trig[2]['similarity'].pop('UH3'), 0.74581409371)
self.assertAlmostEqual(trig[3]['similarity'].pop('UH1'), 1)
self.assertAlmostEqual(trig[3]['similarity'].pop('UH3'), 1)
remaining_results = \
[{'coincidence_sum': 4.0,
'similarity': {},
'stations': ['UH3', 'UH2', 'UH1', 'UH4'],
'time': UTCDateTime(2010, 5, 27, 16, 24, 33, 210000),
'trace_ids': ['BW.UH3..SHZ', 'BW.UH2..SHZ', 'BW.UH1..SHZ',
'BW.UH4..EHZ']},
{'coincidence_sum': 3.0,
'similarity': {},
'stations': ['UH3', 'UH1', 'UH2'],
'time': UTCDateTime(2010, 5, 27, 16, 25, 26, 710000),
'trace_ids': ['BW.UH3..SHZ', 'BW.UH1..SHZ', 'BW.UH2..SHZ']},
{'coincidence_sum': 3.0,
'similarity': {},
'stations': ['UH2', 'UH1', 'UH3'],
'time': UTCDateTime(2010, 5, 27, 16, 27, 2, 260000),
'trace_ids': ['BW.UH2..SHZ', 'BW.UH1..SHZ', 'BW.UH3..SHZ']},
{'coincidence_sum': 4.0,
'similarity': {},
'stations': ['UH3', 'UH2', 'UH1', 'UH4'],
'time': UTCDateTime(2010, 5, 27, 16, 27, 30, 510000),
'trace_ids': ['BW.UH3..SHZ', 'BW.UH2..SHZ', 'BW.UH1..SHZ',
'BW.UH4..EHZ']}]
self.assertTrue(trig == remaining_results)
def test_coincidenceTrigger(self):
"""
Test network coincidence trigger.
"""
st = Stream()
files = [
"BW.UH1._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH2._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH3._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH4._.EHZ.D.2010.147.cut.slist.gz"
]
for filename in files:
filename = os.path.join(self.path, filename)
st += read(filename)
# some prefiltering used for UH network
st.filter('bandpass', freqmin=10, freqmax=20)
# 1. no weighting, no stations specified, good settings
# => 3 events, no false triggers
# for the first test we make some additional tests regarding types
res = coincidenceTrigger("recstalta",
3.5,
1,
st.copy(),
3,
sta=0.5,
lta=10)
self.assertTrue(isinstance(res, list))
self.assertTrue(len(res) == 3)
expected_keys = [
'time', 'coincidence_sum', 'duration', 'stations', 'trace_ids'
]
expected_types = [UTCDateTime, float, float, list, list]
for item in res:
self.assertTrue(isinstance(item, dict))
for key, _type in zip(expected_keys, expected_types):
self.assertTrue(key in item)
self.assertTrue(isinstance(item[key], _type))
self.assertTrue(res[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(res[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(4.2 < res[0]['duration'] < 4.8)
self.assertTrue(res[0]['stations'] == ['UH3', 'UH2', 'UH1', 'UH4'])
self.assertTrue(res[0]['coincidence_sum'] == 4)
self.assertTrue(res[1]['time'] > UTCDateTime("2010-05-27T16:26:59"))
self.assertTrue(res[1]['time'] < UTCDateTime("2010-05-27T16:27:03"))
self.assertTrue(3.2 < res[1]['duration'] < 3.7)
self.assertTrue(res[1]['stations'] == ['UH2', 'UH3', 'UH1'])
self.assertTrue(res[1]['coincidence_sum'] == 3)
self.assertTrue(res[2]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(res[2]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(4.2 < res[2]['duration'] < 4.4)
self.assertTrue(res[2]['stations'] == ['UH3', 'UH2', 'UH1', 'UH4'])
self.assertTrue(res[2]['coincidence_sum'] == 4)
# 2. no weighting, station selection
# => 2 events, no false triggers
trace_ids = ['BW.UH1..SHZ', 'BW.UH3..SHZ', 'BW.UH4..EHZ']
# ignore UserWarnings
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore', UserWarning)
re = coincidenceTrigger("recstalta",
3.5,
1,
st.copy(),
3,
trace_ids=trace_ids,
sta=0.5,
lta=10)
self.assertTrue(len(re) == 2)
self.assertTrue(re[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(re[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(4.2 < re[0]['duration'] < 4.8)
self.assertTrue(re[0]['stations'] == ['UH3', 'UH1', 'UH4'])
self.assertTrue(re[0]['coincidence_sum'] == 3)
self.assertTrue(re[1]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(re[1]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(4.2 < re[1]['duration'] < 4.4)
self.assertTrue(re[1]['stations'] == ['UH3', 'UH1', 'UH4'])
self.assertTrue(re[1]['coincidence_sum'] == 3)
# 3. weighting, station selection
# => 3 events, no false triggers
trace_ids = {
'BW.UH1..SHZ': 0.4,
'BW.UH2..SHZ': 0.35,
'BW.UH3..SHZ': 0.4,
'BW.UH4..EHZ': 0.25
}
res = coincidenceTrigger("recstalta",
3.5,
1,
st.copy(),
1.0,
trace_ids=trace_ids,
sta=0.5,
lta=10)
self.assertTrue(len(res) == 3)
self.assertTrue(res[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(res[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(4.2 < res[0]['duration'] < 4.8)
self.assertTrue(res[0]['stations'] == ['UH3', 'UH2', 'UH1', 'UH4'])
self.assertTrue(res[0]['coincidence_sum'] == 1.4)
self.assertTrue(res[1]['time'] > UTCDateTime("2010-05-27T16:26:59"))
self.assertTrue(res[1]['time'] < UTCDateTime("2010-05-27T16:27:03"))
self.assertTrue(3.2 < res[1]['duration'] < 3.7)
self.assertTrue(res[1]['stations'] == ['UH2', 'UH3', 'UH1'])
self.assertTrue(res[1]['coincidence_sum'] == 1.15)
self.assertTrue(res[2]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(res[2]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(4.2 < res[2]['duration'] < 4.4)
self.assertTrue(res[2]['stations'] == ['UH3', 'UH2', 'UH1', 'UH4'])
self.assertTrue(res[2]['coincidence_sum'] == 1.4)
# 4. weighting, station selection, max_len
# => 2 events, no false triggers, small event does not overlap anymore
trace_ids = {'BW.UH1..SHZ': 0.6, 'BW.UH2..SHZ': 0.6}
# ignore UserWarnings
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore', UserWarning)
re = coincidenceTrigger("recstalta",
3.5,
1,
st.copy(),
1.2,
trace_ids=trace_ids,
max_trigger_length=0.13,
sta=0.5,
lta=10)
self.assertTrue(len(re) == 2)
self.assertTrue(re[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(re[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(0.2 < re[0]['duration'] < 0.3)
self.assertTrue(re[0]['stations'] == ['UH2', 'UH1'])
self.assertTrue(re[0]['coincidence_sum'] == 1.2)
self.assertTrue(re[1]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(re[1]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(0.18 < re[1]['duration'] < 0.2)
self.assertTrue(re[1]['stations'] == ['UH2', 'UH1'])
self.assertTrue(re[1]['coincidence_sum'] == 1.2)
# 5. station selection, extremely sensitive settings
# => 4 events, 1 false triggers
res = coincidenceTrigger("recstalta",
2.5,
1,
st.copy(),
2,
trace_ids=['BW.UH1..SHZ', 'BW.UH3..SHZ'],
sta=0.3,
lta=5)
self.assertTrue(len(res) == 5)
self.assertTrue(res[3]['time'] > UTCDateTime("2010-05-27T16:27:01"))
self.assertTrue(res[3]['time'] < UTCDateTime("2010-05-27T16:27:02"))
self.assertTrue(1.5 < res[3]['duration'] < 1.7)
self.assertTrue(res[3]['stations'] == ['UH3', 'UH1'])
self.assertTrue(res[3]['coincidence_sum'] == 2.0)
# 6. same as 5, gappy stream
# => same as 5 (almost, duration of 1 event changes by 0.02s)
st2 = st.copy()
tr1 = st2.pop(0)
t1 = tr1.stats.starttime
t2 = tr1.stats.endtime
td = t2 - t1
tr1a = tr1.slice(starttime=t1, endtime=t1 + 0.45 * td)
tr1b = tr1.slice(starttime=t1 + 0.6 * td, endtime=t1 + 0.94 * td)
st2.insert(1, tr1a)
st2.insert(3, tr1b)
res = coincidenceTrigger("recstalta",
2.5,
1,
st2,
2,
trace_ids=['BW.UH1..SHZ', 'BW.UH3..SHZ'],
sta=0.3,
lta=5)
self.assertTrue(len(res) == 5)
self.assertTrue(res[3]['time'] > UTCDateTime("2010-05-27T16:27:01"))
self.assertTrue(res[3]['time'] < UTCDateTime("2010-05-27T16:27:02"))
self.assertTrue(1.5 < res[3]['duration'] < 1.7)
self.assertTrue(res[3]['stations'] == ['UH3', 'UH1'])
self.assertTrue(res[3]['coincidence_sum'] == 2.0)
# 7. same as 3 but modify input trace ids and check output of trace_ids
# and other additional information with ``details=True``
st2 = st.copy()
st2[0].stats.network = "XX"
st2[1].stats.location = "99"
st2[1].stats.network = ""
st2[1].stats.location = "99"
st2[1].stats.channel = ""
st2[2].stats.channel = "EHN"
st2[3].stats.network = ""
st2[3].stats.channel = ""
st2[3].stats.station = ""
trace_ids = {
'XX.UH1..SHZ': 0.4,
'.UH2.99.': 0.35,
'BW.UH3..EHN': 0.4,
'...': 0.25
}
res = coincidenceTrigger("recstalta",
3.5,
1,
st2,
1.0,
trace_ids=trace_ids,
details=True,
sta=0.5,
lta=10)
self.assertTrue(len(res) == 3)
self.assertTrue(res[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(res[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(4.2 < res[0]['duration'] < 4.8)
self.assertTrue(res[0]['stations'] == ['UH3', 'UH2', 'UH1', ''])
self.assertTrue(res[0]['trace_ids'][0] == st2[2].id)
self.assertTrue(res[0]['trace_ids'][1] == st2[1].id)
self.assertTrue(res[0]['trace_ids'][2] == st2[0].id)
self.assertTrue(res[0]['trace_ids'][3] == st2[3].id)
self.assertTrue(res[0]['coincidence_sum'] == 1.4)
self.assertTrue(res[1]['time'] > UTCDateTime("2010-05-27T16:26:59"))
self.assertTrue(res[1]['time'] < UTCDateTime("2010-05-27T16:27:03"))
self.assertTrue(3.2 < res[1]['duration'] < 3.7)
self.assertTrue(res[1]['stations'] == ['UH2', 'UH3', 'UH1'])
self.assertTrue(res[1]['trace_ids'][0] == st2[1].id)
self.assertTrue(res[1]['trace_ids'][1] == st2[2].id)
self.assertTrue(res[1]['trace_ids'][2] == st2[0].id)
self.assertTrue(res[1]['coincidence_sum'] == 1.15)
self.assertTrue(res[2]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(res[2]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(4.2 < res[2]['duration'] < 4.4)
self.assertTrue(res[2]['stations'] == ['UH3', 'UH2', 'UH1', ''])
self.assertTrue(res[2]['trace_ids'][0] == st2[2].id)
self.assertTrue(res[2]['trace_ids'][1] == st2[1].id)
self.assertTrue(res[2]['trace_ids'][2] == st2[0].id)
self.assertTrue(res[2]['trace_ids'][3] == st2[3].id)
self.assertTrue(res[2]['coincidence_sum'] == 1.4)
expected_keys = [
'cft_peak_wmean', 'cft_std_wmean', 'cft_peaks', 'cft_stds'
]
expected_types = [float, float, list, list]
for item in res:
for key, _type in zip(expected_keys, expected_types):
self.assertTrue(key in item)
self.assertTrue(isinstance(item[key], _type))
# check some of the detailed info
ev = res[-1]
self.assertAlmostEqual(ev['cft_peak_wmean'], 18.101139518271076)
self.assertAlmostEqual(ev['cft_std_wmean'], 4.800051726246676)
self.assertAlmostEqual(ev['cft_peaks'][0], 18.985548683223936)
self.assertAlmostEqual(ev['cft_peaks'][1], 16.852175794415011)
self.assertAlmostEqual(ev['cft_peaks'][2], 18.64005853900883)
self.assertAlmostEqual(ev['cft_peaks'][3], 17.572363634564621)
self.assertAlmostEqual(ev['cft_stds'][0], 4.8909448258821362)
self.assertAlmostEqual(ev['cft_stds'][1], 4.4446373508521804)
self.assertAlmostEqual(ev['cft_stds'][2], 5.3499401252675964)
self.assertAlmostEqual(ev['cft_stds'][3], 4.2723814539487703)
def cjc_trigger_routine(startdate,enddate,dataloc,trigloc,routype):
"""
Module to run the obspy sta-lta energy based filter routine
Must be parsed start date & end date in obspy UTCDateTime type,
dataloc should be a string of the path for the input data
trigloc should be a string of the ouput path
routype should be a string denpoting the type of detection routine to use
either classic or carl
defaults have been set in the module for trigger parameters
"""
###############################################################################
# Import parameter settings
import sys
sys.path.insert(0,"/home/calumch/my_programs/Building/rt2detection")
from par import trigger_par as defaults
print defaults.stalen
###############################################################################
# Format dates
startyear=startdate.split('/')[0]
startmonth=startdate.split('/')[1]
startday=startdate.split('/')[2]
endyear=enddate.split('/')[0]
endmonth=enddate.split('/')[1]
endday=enddate.split('/')[2]
# Import modules
from obspy import read as obsread
from obspy import UTCDateTime
import glob, os
import numpy as np
from obspy.signal import coincidenceTrigger
# Generate list of days to check through
lengthinseconds=UTCDateTime(endyear+' '+endmonth+' '+endday)-\
UTCDateTime(startyear+' '+startmonth+' '+startday)
lendays=lengthinseconds/86400
lengthinseconds=[]
dfiles=[]
dates=[]
for i in range(0,int(lendays)+1):
dates.append(UTCDateTime(startyear+' '+startmonth+' '+startday)+(i*86400))
dfiles.extend(glob.glob(dataloc+'/'+str(dates[i].year)+'/'+\
str(dates[i].month).zfill(2)+'/'+str(dates[i].year)+'-'+\
str(dates[i].month).zfill(2)+'-'+str(dates[i].day).zfill(2)+'*'))
print len(dfiles)
wavelist=[] # Initialize list variable
# Read in data
for hfile in dfiles:
print 'Working on file: '+hfile
st=obsread(hfile)
st1=st.copy()
if not defaults.comp=='all':
st1=st1.select(channel='*'+defaults.comp)
# De-mean data
for tr in st:
tr.data=tr.data-np.mean(tr.data)
# Filter data
st1.filter('bandpass',freqmin=defaults.lowcut,freqmax=defaults.highcut)
# Use the obspy triggering routine
trig=[]
if routype=='classic':
trig = coincidenceTrigger("recstalta",defaults.trigon,\
defaults.trigoff,st1,defaults.netsum,\
sta=defaults.stalen,lta=defaults.ltalen,\
delete_long_trigger='True',\
trigger_off_extension=\
defaults.netwin)
else:
try:
trig = coincidenceTrigger("carlstatrig",defaults.trigon,\
defaults.trigoff,st1,\
defaults.netsum,sta=defaults.stalen,\
lta=defaults.ltalen,ratio=defaults.crat,\
quiet=defaults.cquite,delete_long_trigger='True')
except:
print 'Triggering routine failed, suggest altering parameters'
# Cut data and write out in multiplexed miniseed files
if trig and defaults.trigout=='Y':
for event in trig:
stout=st.slice(event['time']-defaults.precut,event['time']+defaults.postcut)
filename=str(stout[0].stats.starttime.year)+'-'+\
str(stout[0].stats.starttime.month).zfill(2)+'-'+\
str(stout[0].stats.starttime.day).zfill(2)+'-'+\
str(stout[0].stats.starttime.hour).zfill(2)+\
str(stout[0].stats.starttime.minute).zfill(2)+'-'+\
str(stout[0].stats.starttime.second).zfill(2)+'.'+\
defaults.net+'_'+str(len(stout)).zfill(3)+'_00'
if not os.path.isdir(trigloc+'/'+\
str(stout[0].stats.starttime.year)):
os.makedirs(trigloc+'/'+str(stout[0].stats.starttime.year))
if not os.path.isdir(trigloc+'/'+str(stout[0].stats.starttime.year)\
+'/'+str(stout[0].stats.starttime.month).zfill(2)):
os.makedirs(trigloc+'/'+str(stout[0].stats.starttime.year)\
+'/'+str(stout[0].stats.starttime.month).zfill(2))
filename=trigloc+'/'+str(stout[0].stats.starttime.year)+'/'+\
str(stout[0].stats.starttime.month).zfill(2)+'/'+\
filename
wavelist.append(filename)
try:
stout.write(filename,format="MSEED",encoding="STEIM2")
except:
# Cope with dtype issues
for tr in stout:
tr.data = np.array(tr.data, dtype=np.int32)
stout.write(filename,format='MSEED',encoding='STEIM2')
print 'Written triggered file as: '+filename
elif defaults.trigout=='N':
print 'Triggers will not be written out but I made '+len(trig)+' detections'
elif not trig:
print 'No triggers were detected'
return wavelist
stations = ["AIGLE", "SENIN", "DIX", "LAUCH", "MMK", "SIMPL"]
st = Stream()
for station in stations:
try:
tmp = client.getWaveform("CH", station, "", "[EH]HZ", t, t2,
metadata=True)
except:
print station, "---"
continue
st += tmp
st.taper()
st.filter("bandpass", freqmin=1, freqmax=20)
triglist = coincidenceTrigger("recstalta", 10, 2, st, 4, sta=0.5, lta=10)
print len(triglist), "events triggered."
for trig in triglist:
closest_sta = trig['stations'][0]
tr = st.select(station=closest_sta)[0]
trig['latitude'] = tr.stats.coordinates.latitude
trig['longitude'] = tr.stats.coordinates.longitude
paz_wa = {'sensitivity': 2800, 'zeros': [0j], 'gain': 1,
'poles': [-6.2832-4.7124j, -6.2832+4.7124j]}
for trig in triglist:
t = trig['time']
print "#" * 80
print "Trigger time:", t
def test_coincidenceTriggerWithSimilarityChecking(self):
"""
Test network coincidence trigger with cross correlation similarity
checking of given event templates.
"""
st = Stream()
files = ["BW.UH1._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH2._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH3._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH3._.SHN.D.2010.147.cut.slist.gz",
"BW.UH3._.SHE.D.2010.147.cut.slist.gz",
"BW.UH4._.EHZ.D.2010.147.cut.slist.gz"]
for filename in files:
filename = os.path.join(self.path, filename)
st += read(filename)
# some prefiltering used for UH network
st.filter('bandpass', freqmin=10, freqmax=20)
# set up template event streams
times = ["2010-05-27T16:24:33.095000", "2010-05-27T16:27:30.370000"]
templ = {}
for t in times:
t = UTCDateTime(t)
st_ = st.select(station="UH3").slice(t, t + 2.5).copy()
templ.setdefault("UH3", []).append(st_)
times = ["2010-05-27T16:27:30.574999"]
for t in times:
t = UTCDateTime(t)
st_ = st.select(station="UH1").slice(t, t + 2.5).copy()
templ.setdefault("UH1", []).append(st_)
trace_ids = {"BW.UH1..SHZ": 1,
"BW.UH2..SHZ": 1,
"BW.UH3..SHZ": 1,
"BW.UH4..EHZ": 1}
similarity_thresholds = {"UH1": 0.8, "UH3": 0.7}
with warnings.catch_warnings(record=True) as w:
# avoid getting influenced by the warning filters getting set up
# differently in obspy-runtests.
# (e.g. depending on options "-v" and "-q")
warnings.resetwarnings()
trig = coincidenceTrigger(
"classicstalta", 5, 1, st.copy(), 4, sta=0.5, lta=10,
trace_ids=trace_ids, event_templates=templ,
similarity_threshold=similarity_thresholds)
# two warnings get raised
self.assertEqual(len(w), 2)
# check floats in resulting dictionary separately
self.assertAlmostEqual(trig[0].pop('duration'), 3.9600000381469727)
self.assertAlmostEqual(trig[1].pop('duration'), 1.9900000095367432)
self.assertAlmostEqual(trig[2].pop('duration'), 1.9200000762939453)
self.assertAlmostEqual(trig[3].pop('duration'), 3.9200000762939453)
self.assertAlmostEqual(trig[0]['similarity'].pop('UH1'), 0.94149447384)
self.assertAlmostEqual(trig[0]['similarity'].pop('UH3'), 1)
self.assertAlmostEqual(trig[1]['similarity'].pop('UH1'), 0.65228204570)
self.assertAlmostEqual(trig[1]['similarity'].pop('UH3'), 0.72679293429)
self.assertAlmostEqual(trig[2]['similarity'].pop('UH1'), 0.89404458774)
self.assertAlmostEqual(trig[2]['similarity'].pop('UH3'), 0.74581409371)
self.assertAlmostEqual(trig[3]['similarity'].pop('UH1'), 1)
self.assertAlmostEqual(trig[3]['similarity'].pop('UH3'), 1)
remaining_results = \
[{'coincidence_sum': 4.0,
'similarity': {},
'stations': ['UH3', 'UH2', 'UH1', 'UH4'],
'time': UTCDateTime(2010, 5, 27, 16, 24, 33, 210000),
'trace_ids': ['BW.UH3..SHZ', 'BW.UH2..SHZ', 'BW.UH1..SHZ',
'BW.UH4..EHZ']},
{'coincidence_sum': 3.0,
'similarity': {},
'stations': ['UH3', 'UH1', 'UH2'],
'time': UTCDateTime(2010, 5, 27, 16, 25, 26, 710000),
'trace_ids': ['BW.UH3..SHZ', 'BW.UH1..SHZ', 'BW.UH2..SHZ']},
{'coincidence_sum': 3.0,
'similarity': {},
'stations': ['UH2', 'UH1', 'UH3'],
'time': UTCDateTime(2010, 5, 27, 16, 27, 2, 260000),
'trace_ids': ['BW.UH2..SHZ', 'BW.UH1..SHZ', 'BW.UH3..SHZ']},
{'coincidence_sum': 4.0,
'similarity': {},
'stations': ['UH3', 'UH2', 'UH1', 'UH4'],
'time': UTCDateTime(2010, 5, 27, 16, 27, 30, 510000),
'trace_ids': ['BW.UH3..SHZ', 'BW.UH2..SHZ', 'BW.UH1..SHZ',
'BW.UH4..EHZ']}]
self.assertTrue(trig == remaining_results)
def test_coincidenceTrigger(self):
"""
Test network coincidence trigger.
"""
st = Stream()
files = ["BW.UH1._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH2._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH3._.SHZ.D.2010.147.cut.slist.gz",
"BW.UH4._.EHZ.D.2010.147.cut.slist.gz"]
for filename in files:
filename = os.path.join(self.path, filename)
st += read(filename)
# some prefiltering used for UH network
st.filter('bandpass', freqmin=10, freqmax=20)
# 1. no weighting, no stations specified, good settings
# => 3 events, no false triggers
# for the first test we make some additional tests regarding types
res = coincidenceTrigger("recstalta", 3.5, 1, st.copy(), 3, sta=0.5,
lta=10)
self.assertTrue(isinstance(res, list))
self.assertTrue(len(res) == 3)
expected_keys = ['time', 'coincidence_sum', 'duration', 'stations',
'trace_ids']
expected_types = [UTCDateTime, float, float, list, list]
for item in res:
self.assertTrue(isinstance(item, dict))
for key, _type in zip(expected_keys, expected_types):
self.assertTrue(key in item)
self.assertTrue(isinstance(item[key], _type))
self.assertTrue(res[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(res[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(4.2 < res[0]['duration'] < 4.8)
self.assertTrue(res[0]['stations'] == ['UH3', 'UH2', 'UH1', 'UH4'])
self.assertTrue(res[0]['coincidence_sum'] == 4)
self.assertTrue(res[1]['time'] > UTCDateTime("2010-05-27T16:26:59"))
self.assertTrue(res[1]['time'] < UTCDateTime("2010-05-27T16:27:03"))
self.assertTrue(3.2 < res[1]['duration'] < 3.7)
self.assertTrue(res[1]['stations'] == ['UH2', 'UH3', 'UH1'])
self.assertTrue(res[1]['coincidence_sum'] == 3)
self.assertTrue(res[2]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(res[2]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(4.2 < res[2]['duration'] < 4.4)
self.assertTrue(res[2]['stations'] == ['UH3', 'UH2', 'UH1', 'UH4'])
self.assertTrue(res[2]['coincidence_sum'] == 4)
# 2. no weighting, station selection
# => 2 events, no false triggers
trace_ids = ['BW.UH1..SHZ', 'BW.UH3..SHZ', 'BW.UH4..EHZ']
# ignore UserWarnings
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore', UserWarning)
re = coincidenceTrigger("recstalta", 3.5, 1, st.copy(), 3,
trace_ids=trace_ids, sta=0.5, lta=10)
self.assertTrue(len(re) == 2)
self.assertTrue(re[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(re[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(4.2 < re[0]['duration'] < 4.8)
self.assertTrue(re[0]['stations'] == ['UH3', 'UH1', 'UH4'])
self.assertTrue(re[0]['coincidence_sum'] == 3)
self.assertTrue(re[1]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(re[1]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(4.2 < re[1]['duration'] < 4.4)
self.assertTrue(re[1]['stations'] == ['UH3', 'UH1', 'UH4'])
self.assertTrue(re[1]['coincidence_sum'] == 3)
# 3. weighting, station selection
# => 3 events, no false triggers
trace_ids = {'BW.UH1..SHZ': 0.4, 'BW.UH2..SHZ': 0.35,
'BW.UH3..SHZ': 0.4, 'BW.UH4..EHZ': 0.25}
res = coincidenceTrigger("recstalta", 3.5, 1, st.copy(), 1.0,
trace_ids=trace_ids, sta=0.5, lta=10)
self.assertTrue(len(res) == 3)
self.assertTrue(res[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(res[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(4.2 < res[0]['duration'] < 4.8)
self.assertTrue(res[0]['stations'] == ['UH3', 'UH2', 'UH1', 'UH4'])
self.assertTrue(res[0]['coincidence_sum'] == 1.4)
self.assertTrue(res[1]['time'] > UTCDateTime("2010-05-27T16:26:59"))
self.assertTrue(res[1]['time'] < UTCDateTime("2010-05-27T16:27:03"))
self.assertTrue(3.2 < res[1]['duration'] < 3.7)
self.assertTrue(res[1]['stations'] == ['UH2', 'UH3', 'UH1'])
self.assertTrue(res[1]['coincidence_sum'] == 1.15)
self.assertTrue(res[2]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(res[2]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(4.2 < res[2]['duration'] < 4.4)
self.assertTrue(res[2]['stations'] == ['UH3', 'UH2', 'UH1', 'UH4'])
self.assertTrue(res[2]['coincidence_sum'] == 1.4)
# 4. weighting, station selection, max_len
# => 2 events, no false triggers, small event does not overlap anymore
trace_ids = {'BW.UH1..SHZ': 0.6, 'BW.UH2..SHZ': 0.6}
# ignore UserWarnings
with warnings.catch_warnings(record=True):
warnings.simplefilter('ignore', UserWarning)
re = coincidenceTrigger("recstalta", 3.5, 1, st.copy(), 1.2,
trace_ids=trace_ids,
max_trigger_length=0.13, sta=0.5, lta=10)
self.assertTrue(len(re) == 2)
self.assertTrue(re[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(re[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(0.2 < re[0]['duration'] < 0.3)
self.assertTrue(re[0]['stations'] == ['UH2', 'UH1'])
self.assertTrue(re[0]['coincidence_sum'] == 1.2)
self.assertTrue(re[1]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(re[1]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(0.18 < re[1]['duration'] < 0.2)
self.assertTrue(re[1]['stations'] == ['UH2', 'UH1'])
self.assertTrue(re[1]['coincidence_sum'] == 1.2)
# 5. station selection, extremely sensitive settings
# => 4 events, 1 false triggers
res = coincidenceTrigger("recstalta", 2.5, 1, st.copy(), 2,
trace_ids=['BW.UH1..SHZ', 'BW.UH3..SHZ'],
sta=0.3, lta=5)
self.assertTrue(len(res) == 5)
self.assertTrue(res[3]['time'] > UTCDateTime("2010-05-27T16:27:01"))
self.assertTrue(res[3]['time'] < UTCDateTime("2010-05-27T16:27:02"))
self.assertTrue(1.5 < res[3]['duration'] < 1.7)
self.assertTrue(res[3]['stations'] == ['UH3', 'UH1'])
self.assertTrue(res[3]['coincidence_sum'] == 2.0)
# 6. same as 5, gappy stream
# => same as 5 (almost, duration of 1 event changes by 0.02s)
st2 = st.copy()
tr1 = st2.pop(0)
t1 = tr1.stats.starttime
t2 = tr1.stats.endtime
td = t2 - t1
tr1a = tr1.slice(starttime=t1, endtime=t1 + 0.45 * td)
tr1b = tr1.slice(starttime=t1 + 0.6 * td, endtime=t1 + 0.94 * td)
st2.insert(1, tr1a)
st2.insert(3, tr1b)
res = coincidenceTrigger("recstalta", 2.5, 1, st2, 2,
trace_ids=['BW.UH1..SHZ', 'BW.UH3..SHZ'],
sta=0.3, lta=5)
self.assertTrue(len(res) == 5)
self.assertTrue(res[3]['time'] > UTCDateTime("2010-05-27T16:27:01"))
self.assertTrue(res[3]['time'] < UTCDateTime("2010-05-27T16:27:02"))
self.assertTrue(1.5 < res[3]['duration'] < 1.7)
self.assertTrue(res[3]['stations'] == ['UH3', 'UH1'])
self.assertTrue(res[3]['coincidence_sum'] == 2.0)
# 7. same as 3 but modify input trace ids and check output of trace_ids
# and other additional information with ``details=True``
st2 = st.copy()
st2[0].stats.network = "XX"
st2[1].stats.location = "99"
st2[1].stats.network = ""
st2[1].stats.location = "99"
st2[1].stats.channel = ""
st2[2].stats.channel = "EHN"
st2[3].stats.network = ""
st2[3].stats.channel = ""
st2[3].stats.station = ""
trace_ids = {'XX.UH1..SHZ': 0.4, '.UH2.99.': 0.35,
'BW.UH3..EHN': 0.4, '...': 0.25}
res = coincidenceTrigger("recstalta", 3.5, 1, st2, 1.0,
trace_ids=trace_ids, details=True,
sta=0.5, lta=10)
self.assertTrue(len(res) == 3)
self.assertTrue(res[0]['time'] > UTCDateTime("2010-05-27T16:24:31"))
self.assertTrue(res[0]['time'] < UTCDateTime("2010-05-27T16:24:35"))
self.assertTrue(4.2 < res[0]['duration'] < 4.8)
self.assertTrue(res[0]['stations'] == ['UH3', 'UH2', 'UH1', ''])
self.assertTrue(res[0]['trace_ids'][0] == st2[2].id)
self.assertTrue(res[0]['trace_ids'][1] == st2[1].id)
self.assertTrue(res[0]['trace_ids'][2] == st2[0].id)
self.assertTrue(res[0]['trace_ids'][3] == st2[3].id)
self.assertTrue(res[0]['coincidence_sum'] == 1.4)
self.assertTrue(res[1]['time'] > UTCDateTime("2010-05-27T16:26:59"))
self.assertTrue(res[1]['time'] < UTCDateTime("2010-05-27T16:27:03"))
self.assertTrue(3.2 < res[1]['duration'] < 3.7)
self.assertTrue(res[1]['stations'] == ['UH2', 'UH3', 'UH1'])
self.assertTrue(res[1]['trace_ids'][0] == st2[1].id)
self.assertTrue(res[1]['trace_ids'][1] == st2[2].id)
self.assertTrue(res[1]['trace_ids'][2] == st2[0].id)
self.assertTrue(res[1]['coincidence_sum'] == 1.15)
self.assertTrue(res[2]['time'] > UTCDateTime("2010-05-27T16:27:27"))
self.assertTrue(res[2]['time'] < UTCDateTime("2010-05-27T16:27:33"))
self.assertTrue(4.2 < res[2]['duration'] < 4.4)
self.assertTrue(res[2]['stations'] == ['UH3', 'UH2', 'UH1', ''])
self.assertTrue(res[2]['trace_ids'][0] == st2[2].id)
self.assertTrue(res[2]['trace_ids'][1] == st2[1].id)
self.assertTrue(res[2]['trace_ids'][2] == st2[0].id)
self.assertTrue(res[2]['trace_ids'][3] == st2[3].id)
self.assertTrue(res[2]['coincidence_sum'] == 1.4)
expected_keys = ['cft_peak_wmean', 'cft_std_wmean', 'cft_peaks',
'cft_stds']
expected_types = [float, float, list, list]
for item in res:
for key, _type in zip(expected_keys, expected_types):
self.assertTrue(key in item)
self.assertTrue(isinstance(item[key], _type))
# check some of the detailed info
ev = res[-1]
self.assertAlmostEqual(ev['cft_peak_wmean'], 18.101139518271076)
self.assertAlmostEqual(ev['cft_std_wmean'], 4.800051726246676)
self.assertAlmostEqual(ev['cft_peaks'][0], 18.985548683223936)
self.assertAlmostEqual(ev['cft_peaks'][1], 16.852175794415011)
self.assertAlmostEqual(ev['cft_peaks'][2], 18.64005853900883)
self.assertAlmostEqual(ev['cft_peaks'][3], 17.572363634564621)
self.assertAlmostEqual(ev['cft_stds'][0], 4.8909448258821362)
self.assertAlmostEqual(ev['cft_stds'][1], 4.4446373508521804)
self.assertAlmostEqual(ev['cft_stds'][2], 5.3499401252675964)
self.assertAlmostEqual(ev['cft_stds'][3], 4.2723814539487703)
mutt = []
if st:
# preprocessing, backup original data for plotting at end
st.merge(0)
st.detrend("linear")
for tr in st:
tr.data = tr.data * cosTaper(len(tr), 0.01)
#st.simulate(paz_remove="self", paz_simulate=cornFreq2Paz(1.0), remove_sensitivity=False)
st.sort()
st.filter("bandpass", freqmin=PAR.LOW, freqmax=PAR.HIGH, corners=1, zerophase=True)
st.trim(T1, T2)
st_trigger = st.copy()
st.normalize(global_max=False)
# do the triggering
trig = coincidenceTrigger("recstalta", PAR.ON, PAR.OFF, st_trigger,
thr_coincidence_sum=PAR.MIN_STATIONS,
max_trigger_length=PAR.MAXLEN, trigger_off_extension=PAR.ALLOWANCE,
details=True, sta=PAR.STA, lta=PAR.LTA)
for t in trig:
info = "%s %ss %s %s" % (t['time'].strftime("%Y-%m-%dT%H:%M:%S"), ("%.1f" % t['duration']).rjust(4), ("%i" % t['cft_peak_wmean']).rjust(3), "-".join(t['stations']))
summary.append(info)
tmp = st.slice(t['time'] - 1, t['time'] + t['duration'])
outfilename = "%s/%s_%.1f_%i_%s-%s_%s.png" % (PLOTDIR, t['time'].strftime("%Y-%m-%dT%H:%M:%S"), t['duration'], t['cft_peak_wmean'], len(t['stations']), num_stations, "-".join(t['stations']))
tmp.plot(outfile=outfilename)
mutt += ("-a", outfilename)
summary.append("#" * 79)
summary = "\n".join(summary)
summary += "\n" + "\n".join(("%s=%s" % (k, v) for k, v in PAR.items()))
#print summary
open(SUMMARY, "at").write(summary + "\n")
stations = ["AIGLE", "SENIN", "DIX", "LAUCH", "MMK", "SIMPL"]
st = Stream()
for station in stations:
try:
tmp = client.getWaveform("CH", station, "", "[EH]HZ", t, t2,
metadata=True)
except:
print(station, "---")
continue
st += tmp
st.taper()
st.filter("bandpass", freqmin=1, freqmax=20)
triglist = coincidenceTrigger("recstalta", 10, 2, st, 4, sta=0.5, lta=10)
print(len(triglist), "events triggered.")
for trig in triglist:
closest_sta = trig['stations'][0]
tr = st.select(station=closest_sta)[0]
trig['latitude'] = tr.stats.coordinates.latitude
trig['longitude'] = tr.stats.coordinates.longitude
paz_wa = {'sensitivity': 2800, 'zeros': [0j], 'gain': 1,
'poles': [-6.2832 - 4.7124j, -6.2832 + 4.7124j]}
for trig in triglist:
t = trig['time']
print("#" * 80)
print("Trigger time:", t)
