Пример #1
0
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'])
Пример #2
0
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")
Пример #3
0
 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)
Пример #4
0
 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)
Пример #5
0
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
Пример #7
0
 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)
Пример #8
0
 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)
Пример #9
0
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")
Пример #10
0
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)