New function invoked by autoPyLoT for automated picking of onset times. Main tool for automatic picking!

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Ludger Küperkoch 2015-05-29 16:48:58 +02:00
parent 5be662524f
commit 74682952e7

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Function to run automated picking algorithms using AIC,
HOS and AR prediction. Uses object CharFuns and Picker and
function conglomerate utils.
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
from obspy.core import read
import matplotlib.pyplot as plt
import numpy as np
from pylot.core.pick.CharFuns import *
from pylot.core.pick.Picker import *
from pylot.core.pick.CharFuns import *
from pylot.core.pick import utils
import pdb
def run_autopicking(wfstream, pickparam):
'''
param: wfstream
:type: `~obspy.core.stream.Stream`
param: pickparam
:type: container of picking parameters from input file,
usually autoPyLoT.in
'''
# declaring pickparam variables (only for convenience)
# read your autoPyLoT.in for details!
#special parameters for P picking
algoP = pickparam.getParam('algoP')
iplot = pickparam.getParam('iplot')
pstart = pickparam.getParam('pstart')
pstop = pickparam.getParam('pstop')
thosmw = pickparam.getParam('tlta')
hosorder = pickparam.getParam('hosorder')
tsnrz = pickparam.getParam('tsnrz')
hosorder = pickparam.getParam('hosorder')
bpz1 = pickparam.getParam('bpz1')
bpz2 = pickparam.getParam('bpz2')
pickwinP = pickparam.getParam('pickwinP')
tsmoothP = pickparam.getParam('tsmoothP')
ausP = pickparam.getParam('ausP')
nfacP = pickparam.getParam('nfacP')
tpred1z = pickparam.getParam('tpred1z')
tdet1z = pickparam.getParam('tdet1z')
Parorder = pickparam.getParam('Parorder')
addnoise = pickparam.getParam('addnoise')
Precalcwin = pickparam.getParam('Precalcwin')
minAICPslope = pickparam.getParam('minAICPslope')
minAICPSNR = pickparam.getParam('minAICPSNR')
timeerrorsP = pickparam.getParam('timeerrorsP')
#special parameters for S picking
algoS = pickparam.getParam('algoS')
sstart = pickparam.getParam('sstart')
sstop = pickparam.getParam('sstop')
bph1 = pickparam.getParam('bph1')
bph2 = pickparam.getParam('bph2')
tsnrh = pickparam.getParam('tsnrh')
pickwinS = pickparam.getParam('pickwinS')
tpred1h = pickparam.getParam('tpred1h')
tdet1h = pickparam.getParam('tdet1h')
tpred2h = pickparam.getParam('tpred2h')
tdet2h = pickparam.getParam('tdet2h')
Sarorder = pickparam.getParam('Sarorder')
aictsmoothS = pickparam.getParam('aictsmoothS')
tsmoothS = pickparam.getParam('tsmoothS')
ausS = pickparam.getParam('ausS')
minAICSslope = pickparam.getParam('minAICSslope')
minAICSSNR = pickparam.getParam('minAICSSNR')
Srecalcwin = pickparam.getParam('Srecalcwin')
nfacS = pickparam.getParam('nfacS')
timeerrorsS = pickparam.getParam('timeerrorsS')
# split components
zdat = wfstream.select(component="Z")
edat = wfstream.select(component="E")
ndat = wfstream.select(component="N")
if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
print '##########################################'
print 'run_autopicking: Working on P onset of station %s' % zdat[0].stats.station
print 'Filtering vertical trace ...'
print zdat
z_copy = zdat.copy()
#filter and taper data
tr_filt = zdat[0].copy()
tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1], zerophase=False)
tr_filt.taper(max_percentage=0.05, type='hann')
z_copy[0].data = tr_filt.data
##############################################################
#check length of waveform and compare with cut times
Lc = pstop - pstart
Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
Ldiff = Lwf - Lc
if Ldiff < 0:
print 'run_autopicking: Cutting times are too large for actual waveform!'
print 'Use entire waveform instead!'
pstart = 0
pstop = len(zdat[0].data) * zdat[0].stats.delta
cuttimes = [pstart, pstop]
if algoP == 'HOS':
#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
cf1 = HOScf(z_copy, cuttimes, thosmw, hosorder) #instance of HOScf
elif algoP == 'ARZ':
#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
cf1 = ARZcf(z_copy, cuttimes, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
##############################################################
#calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
#class needs stream object => build it
tr_aic = tr_filt.copy()
tr_aic.data =cf1.getCF()
z_copy[0].data = tr_aic.data
aiccf = AICcf(z_copy, cuttimes) #instance of AICcf
##############################################################
#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
##############################################################
#go on with processing if AIC onset passes quality control
if aicpick.getSlope() >= minAICPslope and aicpick.getSNR() >= minAICPSNR:
print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
(aicpick.getSlope(), aicpick.getSNR())
print 'Go on with refined picking ...'
#re-filter waveform with larger bandpass
print 'run_autopicking: re-filtering vertical trace ...'
z_copy = zdat.copy()
tr_filt = zdat[0].copy()
tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1], zerophase=False)
tr_filt.taper(max_percentage=0.05, type='hann')
z_copy[0].data = tr_filt.data
#############################################################
#re-calculate CF from re-filtered trace in vicinity of initial onset
cuttimes2 = [round(max([aicpick.getpick() - Precalcwin, 0])), \
round(min([len(zdat[0].data) * zdat[0].stats.delta, \
aicpick.getpick() + Precalcwin]))]
if algoP == 'HOS':
#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
cf2 = HOScf(z_copy, cuttimes2, thosmw, hosorder) #instance of HOScf
elif algoP == 'ARZ':
#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
cf2 = ARZcf(z_copy, cuttimes2, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
##############################################################
#get refined onset time from CF2 using class Picker
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP, aicpick.getpick())
#############################################################
#quality assessment
#get earliest and latest possible pick and symmetrized uncertainty
[lpickP, epickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz, refPpick.getpick(), iplot)
#get SNR
[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, refPpick.getpick())
#weight P-onset using symmetric error
if Perror <= timeerrorsP[0]:
Pweight = 0
elif Perror > timeerrorsP[0] and Perror <= timeerrorsP[1]:
Pweight = 1
elif Perror > timeerrorsP[1] and Perror <= timeerrorsP[2]:
Pweight = 2
elif Perror > timeerrorsP[2] and Perror <= timeerrorsP[3]:
Pweight = 3
elif Perror > timeerrorsP[3]:
Pweight = 4
print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f' % (Pweight, SNRP, SNRPdB)
else:
print 'Bad initial (AIC) P-pick, skip this onset!'
Pweight = 4
else:
print 'run_autopicking: No vertical component data available, skipping station!'
return
if edat is not None and ndat is not None and Pweight < 4:
print 'Go on picking S onset ...'
print '##################################################'
print 'Working on S onset of station %s' % edat[0].stats.station
print 'Filtering horizontal traces ...'
#determine time window for calculating CF after P onset
#cuttimesh = [round(refPpick.getpick() + sstart), round(refPpick.getpick() + sstop)]
cuttimesh = [round(max([refPpick.getpick() + sstart, 0])), \
round(min([refPpick.getpick() + sstop, Lwf]))]
if algoS == 'ARH':
print edat, ndat
#re-create stream object including both horizontal components
hdat = edat.copy()
hdat += ndat
h_copy = hdat.copy()
#filter and taper data
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
h_copy[0].data = trH1_filt.data
h_copy[1].data = trH2_filt.data
elif algoS == 'AR3':
print zdat, edat, ndat
#re-create stream object including both horizontal components
hdat = zdat.copy()
hdat += edat
hdat += ndat
h_copy = hdat.copy()
#filter and taper data
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH3_filt = hdat[2].copy()
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
trH3_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
trH3_filt.taper(max_percentage=0.05, type='hann')
h_copy[0].data = trH1_filt.data
h_copy[1].data = trH2_filt.data
h_copy[2].data = trH3_filt.data
##############################################################
if algoS == 'ARH':
#calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
arhcf1 = ARHcf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
elif algoS == 'AR3':
#calculate ARH-CF using subclass AR3cf of class CharcteristicFunction
arhcf1 = AR3Ccf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
##############################################################
#calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
#class needs stream object => build it
tr_arhaic = trH1_filt.copy()
tr_arhaic.data = arhcf1.getCF()
h_copy[0].data = tr_arhaic.data
#calculate ARH-AIC-CF
haiccf = AICcf(h_copy, cuttimesh) #instance of AICcf
##############################################################
#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None, aictsmoothS)
###############################################################
#go on with processing if AIC onset passes quality control
if aicarhpick.getSlope() >= minAICSslope and aicarhpick.getSNR() >= minAICSSNR:
print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \
% (aicarhpick.getSlope(), aicarhpick.getSNR())
print 'Go on with refined picking ...'
#re-calculate CF from re-filtered trace in vicinity of initial onset
cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin), \
round(aicarhpick.getpick() + Srecalcwin)]
#re-filter waveform with larger bandpass
print 'run_autopicking: re-filtering horizontal traces...'
h_copy = hdat.copy()
#filter and taper data
if algoS == 'ARH':
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
h_copy[0].data = trH1_filt.data
h_copy[1].data = trH2_filt.data
#############################################################
arhcf2 = ARHcf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
elif algoS == 'AR3':
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH3_filt = hdat[2].copy()
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
trH3_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
trH3_filt.taper(max_percentage=0.05, type='hann')
h_copy[0].data = trH1_filt.data
h_copy[1].data = trH2_filt.data
h_copy[2].data = trH3_filt.data
#############################################################
arhcf2 = AR3Ccf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
#get refined onset time from CF2 using class Picker
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS, tsmoothS, aicarhpick.getpick())
#############################################################
#quality assessment
#get earliest and latest possible pick and symmetrized uncertainty
h_copy[0].data = trH1_filt.data
[lpickS1, epickS1, Serror1] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
h_copy[0].data = trH2_filt.data
[lpickS2, epickS2, Serror2] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
if algoS == 'ARH':
#get earliest pick of both earliest possible picks
epick = [epickS1, epickS2]
lpick = [lpickS1, lpickS2]
pickerr = [Serror1, Serror2]
ipick =np.argmin([epickS1, epickS2])
elif algoS == 'AR3':
[lpickS3, epickS3, Serror3] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
#get earliest pick of all three picks
epick = [epickS1, epickS2, epickS3]
lpick = [lpickS1, lpickS2, lpickS3]
pickerr = [Serror1, Serror2, Serror3]
ipick =np.argmin([epickS1, epickS2, epickS3])
epickS = epick[ipick]
lpickS = lpick[ipick]
Serror = pickerr[ipick]
#get SNR
[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh, refSpick.getpick())
#weight S-onset using symmetric error
if Serror <= timeerrorsS[0]:
Sweight = 0
elif Serror > timeerrorsS[0] and Serror <= timeerrorsS[1]:
Sweight = 1
elif Perror > timeerrorsS[1] and Serror <= timeerrorsS[2]:
Sweight = 2
elif Serror > timeerrorsS[2] and Serror <= timeerrorsS[3]:
Sweight = 3
elif Serror > timeerrorsS[3]:
Sweight = 4
print 'run_autopicking: S-weight: %d, SNR: %f, SNR[dB]: %f' % (Sweight, SNRS, SNRSdB)
else:
print 'Bad initial (AIC) S-pick, skip this onset!'
Sweight = 4
else:
print 'run_autopicking: No horizontal component data available, skipping S picking!'
return
##############################################################
if iplot > 0:
#plot vertical trace
plt.figure()
plt.subplot(3,1,1)
tdata = np.arange(0, tr_filt.stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta)
p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b')
p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm')
p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r')
p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2)
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.yticks([])
plt.ylim([-1.5, 1.5])
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.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
#plot horizontal traces
plt.subplot(3,1,2)
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
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.yticks([])
plt.ylim([-1.5, 1.5])
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.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
plt.subplot(3,1,3)
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
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.yticks([])
plt.ylim([-1.5, 1.5])
plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
plt.ylabel('Normalized Counts')
plt.title(trH2_filt.stats.channel)
plt.show()
raw_input()
plt.close()