[new] introduced verbose flag to suppress to much output during auto picking (slows down especially to GUI triggered auto picking process)
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f3ba22b235
@ -46,7 +46,7 @@ def autopickevent(data, param):
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# check S-P times (Wadati)
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return wadaticheck(jk_checked_onsets, wdttolerance, iplot)
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def autopickstation(wfstream, pickparam):
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def autopickstation(wfstream, pickparam, verbose=False):
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"""
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:param: wfstream
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:type: `~obspy.core.stream.Stream`
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@ -153,11 +153,11 @@ def autopickstation(wfstream, pickparam):
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ndat = wfstream.select(component="1")
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if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
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print '##########################################'
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print 'autopickstation: Working on P onset of station %s' % zdat[
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0].stats.station
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print 'Filtering vertical trace ...'
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print zdat
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msg = '##########################################\nautopickstation:' \
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' Working on P onset of station {station}\nFiltering vertical ' \
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'trace ...\n{data}'.format(station=zdat[0].stats.station,
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data=str(zdat))
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if verbose: print(msg)
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z_copy = zdat.copy()
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# filter and taper data
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tr_filt = zdat[0].copy()
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@ -171,9 +171,9 @@ def autopickstation(wfstream, pickparam):
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Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
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Ldiff = Lwf - Lc
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if Ldiff < 0:
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print 'autopickstation: Cutting times are too large for actual ' \
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'waveform!'
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print 'Using entire waveform instead!'
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msg = 'autopickstation: Cutting times are too large for actual ' \
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'waveform!\nUsing entire waveform instead!'
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if verbose: print(msg)
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pstart = 0
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pstop = len(zdat[0].data) * zdat[0].stats.delta
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cuttimes = [pstart, pstop]
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@ -206,10 +206,11 @@ def autopickstation(wfstream, pickparam):
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z_copy[0].data = tr_filt.data
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zne = z_copy
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if len(ndat) == 0 or len(edat) == 0:
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print ("One or more horizontal components missing!")
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print ("Signal length only checked on vertical component!")
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print ("Decreasing minsiglengh from %f to %f"
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% (minsiglength, minsiglength / 2))
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msg = 'One or more horizontal components missing!\nSignal ' \
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'length only checked on vertical component!\n' \
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'Decreasing minsiglengh from {0} to ' \
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'{1}'.format(minsiglength, minsiglength / 2)
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if verbose: print(msg)
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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength / 2,
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nfacsl, minpercent, iplot)
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@ -235,8 +236,9 @@ def autopickstation(wfstream, pickparam):
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# check for spuriously picked S onset
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# both horizontal traces needed
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if len(ndat) == 0 or len(edat) == 0:
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print 'One or more horizontal components missing!'
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print 'Skipping control function checkZ4S.'
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msg = 'One or more horizontal components missing!\n' \
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'Skipping control function checkZ4S.'
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if verbose: print(msg)
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else:
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Pflag = checkZ4S(zne, aicpick.getpick(), zfac,
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tsnrz[3], iplot)
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@ -252,11 +254,12 @@ def autopickstation(wfstream, pickparam):
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aicpick.getSNR() >= minAICPSNR and
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Pflag == 1):
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aicPflag = 1
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print 'AIC P-pick passes quality control: Slope: %f counts/s, SNR: %f' % \
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(aicpick.getSlope(), aicpick.getSNR())
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print 'Go on with refined picking ...'
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msg = 'AIC P-pick passes quality control: Slope: {0} counts/s, ' \
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'SNR: {1}\nGo on with refined picking ...\n' \
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'autopickstation: re-filtering vertical trace ' \
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'...'.format(aicpick.getSlope(), aicpick.getSNR())
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if verbose: print(msg)
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# re-filter waveform with larger bandpass
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print 'autopickstation: re-filtering vertical trace ...'
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z_copy = zdat.copy()
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tr_filt = zdat[0].copy()
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tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1],
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@ -334,9 +337,10 @@ def autopickstation(wfstream, pickparam):
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# waveform after P onset!
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zc = crossings_nonzero_all(wfzc)
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if np.size(zc) == 0 or len(zc) <= 3:
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print ("Something is wrong with the waveform, "
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"no zero crossings derived!")
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print ("Cannot calculate source spectrum!")
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msg = "Something is wrong with the waveform, " \
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"no zero crossings derived!\nCannot " \
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"calculate source spectrum!"
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if verbose: print(msg)
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else:
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index = min([3, len(zc) - 1])
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calcwin = (zc[index] - zc[0]) * z_copy[0].stats.delta
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@ -345,33 +349,42 @@ def autopickstation(wfstream, pickparam):
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w0 = specpara.getw0()
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fc = specpara.getfc()
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print ("autopickstation: P-weight: %d, SNR: %f, SNR[dB]: %f, "
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"Polarity: %s" % (Pweight, SNRP, SNRPdB, FM))
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msg = "autopickstation: P-weight: {0}, " \
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"SNR: {1}, SNR[dB]: {2}, Polarity: {3}".format(Pweight,
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SNRP,
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SNRPdB,
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FM)
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print(msg)
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Sflag = 1
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else:
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print ("Bad initial (AIC) P-pick, skipping this onset!")
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print 'AIC-SNR=', aicpick.getSNR(), 'AIC-Slope=', aicpick.getSlope(), 'counts/s'
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print '(min. AIC-SNR=', minAICPSNR, ', min. AIC-Slope=', minAICPslope, 'counts/s)'
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msg = 'Bad initial (AIC) P-pick, skipping this onset!\n' \
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'AIC-SNR={0}, AIC-Slope={1}counts/s\n' \
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'(min. AIC-SNR={2}, ' \
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'min. AIC-Slope={3}counts/s)'.format(aicpick.getSNR(),
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aicpick.getSlope(),
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minAICPSNR,
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minAICPslope)
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if verbose: print(msg)
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Sflag = 0
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else:
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print ("autopickstation: No vertical component data available!, "
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"Skipping station!")
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print("autopickstation: No vertical component data available!, "
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"Skipping station!")
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if edat is not None and ndat is not None and len(edat) > 0 and len(
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ndat) > 0 and Pweight < 4:
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print ("Go on picking S onset ...")
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print ("##################################################")
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print ("Working on S onset of station %s" % edat[0].stats.station)
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print ("Filtering horizontal traces ...")
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msg = "Go on picking S onset ...\n" \
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"##################################################\n" \
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"Working on S onset of station {0}\nFiltering horizontal " \
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"traces ...".format(edat[0].stats.station)
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if verbose: print(msg)
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# determine time window for calculating CF after P onset
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cuttimesh = [round(max([mpickP + sstart, 0])),
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round(min([mpickP + sstop, Lwf]))]
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if algoS == 'ARH':
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print edat, ndat
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if verbose: print(edat, ndat)
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# re-create stream object including both horizontal components
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hdat = edat.copy()
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hdat += ndat
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@ -388,7 +401,7 @@ def autopickstation(wfstream, pickparam):
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h_copy[0].data = trH1_filt.data
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h_copy[1].data = trH2_filt.data
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elif algoS == 'AR3':
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print zdat, edat, ndat
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if verbose: print(zdat, edat, ndat)
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# re-create stream object including all components
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hdat = zdat.copy()
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hdat += edat
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@ -441,15 +454,15 @@ def autopickstation(wfstream, pickparam):
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aicarhpick.getSNR() >= minAICSSNR and
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aicarhpick.getpick() is not None):
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aicSflag = 1
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print 'AIC S-pick passes quality control: Slope: %f counts/s, SNR: %f' \
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% (aicarhpick.getSlope(), aicarhpick.getSNR())
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print 'Go on with refined picking ...'
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msg = 'AIC S-pick passes quality control: Slope: {0} counts/s, ' \
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'SNR: {1}\nGo on with refined picking ...\n' \
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'autopickstation: re-filtering horizontal traces ' \
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'...'.format(aicarhpick.getSlope(), aicarhpick.getSNR())
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# re-calculate CF from re-filtered trace in vicinity of initial
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# onset
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cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin),
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round(aicarhpick.getpick() + Srecalcwin)]
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# re-filter waveform with larger bandpass
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print 'autopickstation: re-filtering horizontal traces...'
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h_copy = hdat.copy()
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# filter and taper data
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if algoS == 'ARH':
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@ -554,11 +567,12 @@ def autopickstation(wfstream, pickparam):
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elif Serror > timeerrorsS[3]:
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Sweight = 4
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print 'autopickstation: S-weight: %d, SNR: %f, SNR[dB]: %f' % (
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Sweight, SNRS, SNRSdB)
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print('autopickstation: S-weight: {0}, SNR: {1}, '
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'SNR[dB]: {2}\n'
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'################################################'
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''.format(Sweight, SNRS, SNRSdB))
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##################################################################
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# get Wood-Anderson peak-to-peak amplitude
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print "################################################"
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# initialize Data object
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data = Data()
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# re-create stream object including both horizontal components
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@ -580,15 +594,19 @@ def autopickstation(wfstream, pickparam):
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Ao = wapp.getwapp()
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else:
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print 'Bad initial (AIC) S-pick, skipping this onset!'
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print 'AIC-SNR=', aicarhpick.getSNR(), \
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'AIC-Slope=', aicarhpick.getSlope(), 'counts/s'
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print '(min. AIC-SNR=', minAICSSNR, ', min. AIC-Slope=', \
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minAICSslope, 'counts/s)'
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msg = 'Bad initial (AIC) S-pick, skipping this onset!\n' \
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'AIC-SNR={0}, AIC-Slope={1}counts/s\n' \
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'(min. AIC-SNR={2}, ' \
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'min. AIC-Slope={3}counts/s)\n' \
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'################################################' \
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''.format(aicarhpick.getSNR(),
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aicarhpick.getSlope(),
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minAICSSNR,
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minAICSslope)
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if verbose: print(msg)
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############################################################
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# get Wood-Anderson peak-to-peak amplitude
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print "################################################"
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# initialize Data object
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data = Data()
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# re-create stream object including both horizontal components
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@ -604,8 +622,8 @@ def autopickstation(wfstream, pickparam):
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Ao = wapp.getwapp()
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else:
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print 'autopickstation: No horizontal component data available or ' \
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'bad P onset, skipping S picking!'
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print('autopickstation: No horizontal component data available or ' \
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'bad P onset, skipping S picking!')
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##############################################################
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if iplot > 0:
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@ -819,12 +837,13 @@ def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter):
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:param badpicks: picks to be repicked
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:param pickparameter: picking parameters from autoPyLoT-input file
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:param pickparameter: picking parameters from autoPyLoT-input file
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'''
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print("#######################################################")
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print("autoPyLoT: Found %d bad onsets at station(s) %s, starting re-picking them ...") \
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% (len(badpicks), badpicks)
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msg = '#######################################################\n' \
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'autoPyLoT: Found {0} bad onsets at station(s) {1}, ' \
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'starting re-picking them ...'.format(len(badpicks), badpicks)
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print(msg)
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newpicks = {}
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for i in range(0, len(badpicks)):
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@ -867,13 +886,13 @@ def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter):
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print("Precalcwin: %fs => %fs" % (Precalcwin_old, pickparameter.getParam('Precalcwin')))
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print("noisefactor: %f => %f" % (noisefactor_old, pickparameter.getParam('noisefactor')))
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print("zfac: %f => %f" % (zfac_old, pickparameter.getParam('zfac')))
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# repick station
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newpicks = autopickstation(wf2pick, pickparameter)
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# replace old dictionary with new one
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picks[badpicks[i][0]] = newpicks
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# reset temporary change of picking parameters
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print("iteratepicker: Resetting picking parameters ...")
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pickparameter.setParam(pstart=pstart_old)
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