Implemented first-motion picker, some debugging.
This commit is contained in:
Ludger Küperkoch
parent
ab9a49a727
commit
85f0717f10
@ -77,7 +77,10 @@ def run_autopicking(wfstream, pickparam):
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Srecalcwin = pickparam.getParam('Srecalcwin')
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nfacS = pickparam.getParam('nfacS')
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timeerrorsS = pickparam.getParam('timeerrorsS')
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#parameters for first-motion determination
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minFMSNR = pickparam.getParam('minFMSNR')
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fmpickwin = pickparam.getParam('fmpickwin')
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minfmweight = pickparam.getParam('minfmweight')
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# split components
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zdat = wfstream.select(component="Z")
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@ -125,6 +128,7 @@ def run_autopicking(wfstream, pickparam):
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##############################################################
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#go on with processing if AIC onset passes quality control
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if aicpick.getSlope() >= minAICPslope and aicpick.getSNR() >= minAICPSNR:
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aicPflag = 1
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print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
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(aicpick.getSlope(), aicpick.getSNR())
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print 'Go on with refined picking ...'
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@ -169,11 +173,28 @@ def run_autopicking(wfstream, pickparam):
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elif Perror > timeerrorsP[3]:
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Pweight = 4
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print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f' % (Pweight, SNRP, SNRPdB)
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##############################################################
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#get first motion of P onset
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#certain quality required
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if Pweight <= minfmweight and SNRP >= minFMSNR:
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FM = fmpicker(zdat, z_copy, fmpickwin, refPpick.getpick(), iplot)
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else:
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FM = 'N'
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print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f, Polarity: %s' % (Pweight, SNRP, SNRPdB, FM)
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else:
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print 'Bad initial (AIC) P-pick, skip this onset!'
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print 'AIC-SNR=%f, AIC-Slope=%f' % (aicpick.getSNR(), aicpick.getSlope())
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Pweight = 4
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Sweight = 4
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FM = 'N'
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SNRP = None
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SNRPdB = None
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SNRS = None
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SNRSdB = None
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aicSflag = 0
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aicPflag = 0
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else:
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print 'run_autopicking: No vertical component data available, skipping station!'
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return
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@ -245,6 +266,7 @@ def run_autopicking(wfstream, pickparam):
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###############################################################
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#go on with processing if AIC onset passes quality control
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if aicarhpick.getSlope() >= minAICSslope and aicarhpick.getSNR() >= minAICSSNR:
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aicSflag = 1
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print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \
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% (aicarhpick.getSlope(), aicarhpick.getSNR())
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print 'Go on with refined picking ...'
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@ -327,7 +349,11 @@ def run_autopicking(wfstream, pickparam):
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else:
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print 'Bad initial (AIC) S-pick, skip this onset!'
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print 'AIC-SNR=%f, AIC-Slope=%f' % (aicarhpick.getSNR(), aicarhpick.getSlope())
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Sweight = 4
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SNRS = None
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SNRSdB = None
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aicSflag = 0
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else:
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print 'run_autopicking: No horizontal component data available, skipping S picking!'
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@ -338,59 +364,88 @@ def run_autopicking(wfstream, pickparam):
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#plot vertical trace
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plt.figure()
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plt.subplot(3,1,1)
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tdata = np.arange(0, tr_filt.stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta)
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tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta)
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#check equal length of arrays, sometimes they are different!?
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wfldiff = len(tr_filt.data) - len(tdata)
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if wfldiff < 0:
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tdata = tdata[0:len(tdata) - abs(wfldiff)]
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p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
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p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b')
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p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm')
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p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r')
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plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r')
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plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r')
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p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
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plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2)
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plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [-1.3, -1.3], 'r', linewidth=2)
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plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
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plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
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if Pweight < 4:
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p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b')
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if aicPflag == 1:
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p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm')
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p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r')
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plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r')
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plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r')
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p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
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plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2)
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plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [-1.3, -1.3], 'r', linewidth=2)
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plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
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plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
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plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f Polarity: %s' % (tr_filt.stats.station, \
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tr_filt.stats.channel, Pweight, SNRP, SNRPdB, FM))
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else:
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plt.legend([p1, p2], ['Data', 'CF1'])
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plt.title('%s, P Weight=%d, SNR=None, SNRdB=None' % (tr_filt.stats.channel, Pweight))
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plt.yticks([])
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plt.ylim([-1.5, 1.5])
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plt.ylabel('Normalized Counts')
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plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (tr_filt.stats.station, \
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tr_filt.stats.channel, Pweight, SNRP, SNRPdB))
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plt.suptitle(tr_filt.stats.starttime)
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
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#plot horizontal traces
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plt.subplot(3,1,2)
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th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
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#check equal length of arrays, sometimes they are different!?
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wfldiff = len(trH1_filt.data) - len(th1data)
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if wfldiff < 0:
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th1data = th1data[0:len(th1data) - abs(wfldiff)]
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p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
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p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
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p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
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p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
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plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
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plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
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p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
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plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
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plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
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plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
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plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
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if Pweight < 4:
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p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
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if aicSflag == 1:
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p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
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p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
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plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
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plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
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p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
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plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
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plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
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plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
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plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
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plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
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plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (trH1_filt.stats.channel, \
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Sweight, SNRS, SNRSdB))
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else:
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plt.legend([p21, p22], ['Data', 'CF1'])
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plt.title('%s, S Weight=%d, SNR=None, SNRdB=None' % (trH1_filt.stats.channel, Sweight))
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plt.yticks([])
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plt.ylim([-1.5, 1.5])
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plt.ylabel('Normalized Counts')
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plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (trH1_filt.stats.channel, \
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Sweight, SNRS, SNRSdB))
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plt.suptitle(trH1_filt.stats.starttime)
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plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
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plt.subplot(3,1,3)
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th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
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#check equal length of arrays, sometimes they are different!?
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wfldiff = len(trH2_filt.data) - len(th2data)
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if wfldiff < 0:
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th2data = th2data[0:len(th2data) - abs(wfldiff)]
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plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
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plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
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plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
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plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
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plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
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plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
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plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
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plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
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plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
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plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
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plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
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if Pweight < 4:
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p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
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if aicSflag == 1:
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p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
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p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
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plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
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plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
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p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
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plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
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plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
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plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
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plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
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plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
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else:
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plt.legend([p21, p22], ['Data', 'CF1'])
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plt.yticks([])
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plt.ylim([-1.5, 1.5])
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plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
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