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Implemented first-motion picker, some debugging.

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This commit is contained in:
Ludger Küperkoch 2015-06-01 14:18:18 +02:00
parent ab9a49a727
commit 85f0717f10
1 changed files with 94 additions and 39 deletions
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133
pylot/core/pick/run_autopicking.py
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@ -77,7 +77,10 @@ def run_autopicking(wfstream, pickparam):
Srecalcwin = pickparam.getParam('Srecalcwin') Srecalcwin = pickparam.getParam('Srecalcwin')
nfacS = pickparam.getParam('nfacS') nfacS = pickparam.getParam('nfacS')
timeerrorsS = pickparam.getParam('timeerrorsS') timeerrorsS = pickparam.getParam('timeerrorsS')
#parameters for first-motion determination
minFMSNR = pickparam.getParam('minFMSNR')
fmpickwin = pickparam.getParam('fmpickwin')
minfmweight = pickparam.getParam('minfmweight')
# split components # split components
zdat = wfstream.select(component="Z") zdat = wfstream.select(component="Z")
@ -125,6 +128,7 @@ def run_autopicking(wfstream, pickparam):
############################################################## ##############################################################
#go on with processing if AIC onset passes quality control #go on with processing if AIC onset passes quality control
if aicpick.getSlope() >= minAICPslope and aicpick.getSNR() >= minAICPSNR: if aicpick.getSlope() >= minAICPslope and aicpick.getSNR() >= minAICPSNR:
aicPflag = 1
print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \ print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
(aicpick.getSlope(), aicpick.getSNR()) (aicpick.getSlope(), aicpick.getSNR())
print 'Go on with refined picking ...' print 'Go on with refined picking ...'
@ -169,11 +173,28 @@ def run_autopicking(wfstream, pickparam):
elif Perror > timeerrorsP[3]: elif Perror > timeerrorsP[3]:
Pweight = 4 Pweight = 4
print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f' % (Pweight, SNRP, SNRPdB) ##############################################################
#get first motion of P onset
#certain quality required
if Pweight <= minfmweight and SNRP >= minFMSNR:
FM = fmpicker(zdat, z_copy, fmpickwin, refPpick.getpick(), iplot)
else:
FM = 'N'
print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f, Polarity: %s' % (Pweight, SNRP, SNRPdB, FM)
else: else:
print 'Bad initial (AIC) P-pick, skip this onset!' print 'Bad initial (AIC) P-pick, skip this onset!'
print 'AIC-SNR=%f, AIC-Slope=%f' % (aicpick.getSNR(), aicpick.getSlope())
Pweight = 4 Pweight = 4
Sweight = 4
FM = 'N'
SNRP = None
SNRPdB = None
SNRS = None
SNRSdB = None
aicSflag = 0
aicPflag = 0
else: else:
print 'run_autopicking: No vertical component data available, skipping station!' print 'run_autopicking: No vertical component data available, skipping station!'
return return
@ -245,6 +266,7 @@ def run_autopicking(wfstream, pickparam):
############################################################### ###############################################################
#go on with processing if AIC onset passes quality control #go on with processing if AIC onset passes quality control
if aicarhpick.getSlope() >= minAICSslope and aicarhpick.getSNR() >= minAICSSNR: if aicarhpick.getSlope() >= minAICSslope and aicarhpick.getSNR() >= minAICSSNR:
aicSflag = 1
print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \ print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \
% (aicarhpick.getSlope(), aicarhpick.getSNR()) % (aicarhpick.getSlope(), aicarhpick.getSNR())
print 'Go on with refined picking ...' print 'Go on with refined picking ...'
@ -327,7 +349,11 @@ def run_autopicking(wfstream, pickparam):
else: else:
print 'Bad initial (AIC) S-pick, skip this onset!' print 'Bad initial (AIC) S-pick, skip this onset!'
print 'AIC-SNR=%f, AIC-Slope=%f' % (aicarhpick.getSNR(), aicarhpick.getSlope())
Sweight = 4 Sweight = 4
SNRS = None
SNRSdB = None
aicSflag = 0
else: else:
print 'run_autopicking: No horizontal component data available, skipping S picking!' print 'run_autopicking: No horizontal component data available, skipping S picking!'
@ -338,59 +364,88 @@ def run_autopicking(wfstream, pickparam):
#plot vertical trace #plot vertical trace
plt.figure() plt.figure()
plt.subplot(3,1,1) plt.subplot(3,1,1)
tdata = np.arange(0, tr_filt.stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta) tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta)
#check equal length of arrays, sometimes they are different!?
wfldiff = len(tr_filt.data) - len(tdata)
if wfldiff < 0:
tdata = tdata[0:len(tdata) - abs(wfldiff)]
p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k') p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b') if Pweight < 4:
p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm') p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b')
p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r') if aicPflag == 1:
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r') p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r') p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r')
p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2) plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r')
plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2) plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r')
plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [-1.3, -1.3], 'r', linewidth=2) p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--') plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2)
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--') plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [-1.3, -1.3], 'r', linewidth=2)
plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
plt.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f Polarity: %s' % (tr_filt.stats.station, \
tr_filt.stats.channel, Pweight, SNRP, SNRPdB, FM))
else:
plt.legend([p1, p2], ['Data', 'CF1'])
plt.title('%s, P Weight=%d, SNR=None, SNRdB=None' % (tr_filt.stats.channel, Pweight))
plt.yticks([]) plt.yticks([])
plt.ylim([-1.5, 1.5]) plt.ylim([-1.5, 1.5])
plt.ylabel('Normalized Counts') plt.ylabel('Normalized Counts')
plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (tr_filt.stats.station, \
tr_filt.stats.channel, Pweight, SNRP, SNRPdB))
plt.suptitle(tr_filt.stats.starttime) plt.suptitle(tr_filt.stats.starttime)
plt.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
#plot horizontal traces #plot horizontal traces
plt.subplot(3,1,2) plt.subplot(3,1,2)
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta) th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
#check equal length of arrays, sometimes they are different!?
wfldiff = len(trH1_filt.data) - len(th1data)
if wfldiff < 0:
th1data = th1data[0:len(th1data) - abs(wfldiff)]
p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k') p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b') if Pweight < 4:
p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm') p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g') if aicSflag == 1:
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g') p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g') p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2) plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2) plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2) p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--') plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--') plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (trH1_filt.stats.channel, \
Sweight, SNRS, SNRSdB))
else:
plt.legend([p21, p22], ['Data', 'CF1'])
plt.title('%s, S Weight=%d, SNR=None, SNRdB=None' % (trH1_filt.stats.channel, Sweight))
plt.yticks([]) plt.yticks([])
plt.ylim([-1.5, 1.5]) plt.ylim([-1.5, 1.5])
plt.ylabel('Normalized Counts') plt.ylabel('Normalized Counts')
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (trH1_filt.stats.channel, \
Sweight, SNRS, SNRSdB))
plt.suptitle(trH1_filt.stats.starttime) plt.suptitle(trH1_filt.stats.starttime)
plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
plt.subplot(3,1,3) plt.subplot(3,1,3)
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta) th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
#check equal length of arrays, sometimes they are different!?
wfldiff = len(trH2_filt.data) - len(th2data)
if wfldiff < 0:
th2data = th2data[0:len(th2data) - abs(wfldiff)]
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k') plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b') if Pweight < 4:
plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm') p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g') if aicSflag == 1:
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g') p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g') p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2) plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2) plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2) p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--') plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--') plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
else:
plt.legend([p21, p22], ['Data', 'CF1'])
plt.yticks([]) plt.yticks([])
plt.ylim([-1.5, 1.5]) plt.ylim([-1.5, 1.5])
plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime) plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)