Merge branch 'develop' of ariadne:/data/git/pylot into 176
This commit is contained in:
commit
49cbfd92e5
@ -30,8 +30,8 @@ HYPOSAT #locrt# %location routine used ("HYPO
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300 #Qp# %quality factor for P waves
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100 #Qs# %quality factor for S waves
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#common settings picker#
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20 #pstart# %start time [s] for calculating CF for P-picking
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80 #pstop# %end time [s] for calculating CF for P-picking
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15 #pstart# %start time [s] for calculating CF for P-picking
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60 #pstop# %end time [s] for calculating CF for P-picking
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-1.0 #sstart# %start time [s] after or before(-) P-onset for calculating CF for S-picking
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7 #sstop# %end time [s] after P-onset for calculating CF for S-picking
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2 20 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
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@ -81,14 +81,14 @@ ARH #algoS# %choose algorithm for S-onset
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#inital AIC onset#
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0.01 0.02 0.04 0.08 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
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0.04 0.08 0.16 0.32 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
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50 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
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10 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
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1.2 #minAICPSNR# %below this SNR the initial P pick is rejected
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6 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
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1.5 #minAICSSNR# %below this SNR the initial S pick is rejected
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#check duration of signal using envelope function#
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2.5 #minsiglength# %minimum required length of signal [s]
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3 #noisefactor# %noiselevel*noisefactor=threshold
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70 #minpercent# %required percentage of samples higher than threshold
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5 #minsiglength# %minimum required length of signal [s]
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1.8 #noisefactor# %noiselevel*noisefactor=threshold
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50 #minpercent# %required percentage of samples higher than threshold
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#check for spuriously picked S-onsets#
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2.0 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
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#check statistics of P onsets#
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@ -9,6 +9,7 @@
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EVENT_DATA/LOCAL #datapath# %data path
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2006.01_Nisyros #database# %name of data base
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e1412.008.06 #eventID# %event ID for single event processing
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/DATA/Egelados/STAT_INFO #invdir# %full path to inventory or dataless-seed file
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PILOT #datastructure# %choose data structure
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0 #iplot# %flag for plotting: 0 none, 1, partly, >1 everything
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AUTOPHASES_AIC_HOS4_ARH #phasefile# %name of autoPILOT output phase file
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@ -30,8 +31,8 @@ HYPOSAT #locrt# %location routine used ("HYPO
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100 #Qs# %quality factor for S waves
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#common settings picker#
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20 #pstart# %start time [s] for calculating CF for P-picking
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160 #pstop# %end time [s] for calculating CF for P-picking
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3.0 #sstart# %start time [s] after or before(-) P-onset for calculating CF for S-picking
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100 #pstop# %end time [s] for calculating CF for P-picking
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1.0 #sstart# %start time [s] after or before(-) P-onset for calculating CF for S-picking
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100 #sstop# %end time [s] after P-onset for calculating CF for S-picking
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3 10 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
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3 12 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
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@ -64,11 +65,11 @@ ARH #algoS# %choose algorithm for S-onset
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0.3 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
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4 #Sarorder# %for AR-picker, order of AR process of H-components
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10 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
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6 #pickwinS# %for initial AIC and refined pick, length of S-pick window [s]
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25 #pickwinS# %for initial AIC and refined pick, length of S-pick window [s]
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5 0.2 3.0 3.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
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3.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
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3.5 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
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1.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
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0.4 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
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0.2 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
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1.5 #nfacS# %for AR-picker, noise factor for noise level determination (S)
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%first-motion picker%
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1 #minfmweight# %minimum required p weight for first-motion determination
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@ -78,18 +79,18 @@ ARH #algoS# %choose algorithm for S-onset
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#inital AIC onset#
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0.04 0.08 0.16 0.32 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
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0.04 0.08 0.16 0.32 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
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5 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
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3 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
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1.2 #minAICPSNR# %below this SNR the initial P pick is rejected
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8 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
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1.5 #minAICSSNR# %below this SNR the initial S pick is rejected
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5 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
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2.5 #minAICSSNR# %below this SNR the initial S pick is rejected
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#check duration of signal using envelope function#
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6 #minsiglength# %minimum required length of signal [s]
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1.5 #noisefactor# %noiselevel*noisefactor=threshold
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30 #minsiglength# %minimum required length of signal [s]
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2.5 #noisefactor# %noiselevel*noisefactor=threshold
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60 #minpercent# %required percentage of samples higher than threshold
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#check for spuriously picked S-onsets#
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1.5 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
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1.0 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
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#check statistics of P onsets#
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35 #mdttolerance# %maximum allowed deviation of P picks from median [s]
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45 #mdttolerance# %maximum allowed deviation of P picks from median [s]
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#wadati check#
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2.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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3.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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@ -18,6 +18,7 @@ calculated after Diehl & Kissling (2009).
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from pylot.core.pick.utils import getnoisewin, getsignalwin
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@ -245,8 +246,7 @@ class AICPicker(AutoPicking):
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if datafit[0] >= datafit[len(datafit) - 1]:
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print 'AICPicker: Negative slope, bad onset skipped!'
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return
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self.slope = 1 / tslope * datafit[len(dataslope) - 1] - datafit[0]
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self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0])
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else:
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self.SNR = None
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@ -41,9 +41,9 @@ def autopickevent(data, param):
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# quality control
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# median check and jackknife on P-onset times
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jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, 2)
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jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, iplot)
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# check S-P times (Wadati)
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return wadaticheck(jk_checked_onsets, wdttolerance, 2)
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return wadaticheck(jk_checked_onsets, wdttolerance, iplot)
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def autopickstation(wfstream, pickparam):
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"""
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@ -196,16 +196,18 @@ def autopickstation(wfstream, pickparam):
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##############################################################
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if aicpick.getpick() is not None:
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# check signal length to detect spuriously picked noise peaks
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# use all available components to avoid skipping correct picks
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# on vertical traces with weak P coda
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z_copy[0].data = tr_filt.data
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Pflag = checksignallength(z_copy, aicpick.getpick(), tsnrz,
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minsiglength, \
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nfacsl, minpercent, iplot)
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if Pflag == 1:
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# check for spuriously picked S onset
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# both horizontal traces needed
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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 'Skipping control function checkZ4S.'
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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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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength / 2, \
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nfacsl, minpercent, iplot)
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else:
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# filter and taper horizontal traces
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trH1_filt = edat.copy()
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@ -218,9 +220,19 @@ def autopickstation(wfstream, pickparam):
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zerophase=False)
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trH1_filt.taper(max_percentage=0.05, type='hann')
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trH2_filt.taper(max_percentage=0.05, type='hann')
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zne = z_copy
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zne += trH1_filt
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zne += trH2_filt
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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength, \
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nfacsl, minpercent, iplot)
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if Pflag == 1:
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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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else:
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Pflag = checkZ4S(zne, aicpick.getpick(), zfac, \
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tsnrz[3], iplot)
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if Pflag == 0:
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@ -515,9 +527,10 @@ def autopickstation(wfstream, pickparam):
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hdat = edat.copy()
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hdat += ndat
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h_copy = hdat.copy()
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cordat = data.restituteWFData(invdir, h_copy)
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[cordat, restflag] = data.restituteWFData(invdir, h_copy)
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# calculate WA-peak-to-peak amplitude
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# using subclass WApp of superclass Magnitude
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if restflag == 1:
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if Sweight < 4:
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wapp = WApp(cordat, mpickS, mpickP + sstop, iplot)
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else:
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@ -544,7 +557,8 @@ def autopickstation(wfstream, pickparam):
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hdat = edat.copy()
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hdat += ndat
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h_copy = hdat.copy()
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cordat = data.restituteWFData(invdir, h_copy)
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[cordat, restflag] = data.restituteWFData(invdir, h_copy)
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if restflag == 1:
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# calculate WA-peak-to-peak amplitude
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# using subclass WApp of superclass Magnitude
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wapp = WApp(cordat, mpickP, mpickP + sstop + (0.5 * (mpickP \
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@ -9,7 +9,6 @@
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"""
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import numpy as np
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import scipy as sc
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import matplotlib.pyplot as plt
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from obspy.core import Stream, UTCDateTime
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import warnings
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@ -44,7 +43,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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LPick = None
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EPick = None
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PickError = None
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print 'earllatepicker: Get earliest and latest possible pick relative to most likely pick ...'
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print ("earllatepicker: Get earliest and latest possible pick relative to most likely pick ...")
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x = X[0].data
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t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
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@ -60,8 +59,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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ilup, = np.where(x[isignal] > nlevel)
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ildown, = np.where(x[isignal] < -nlevel)
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if not ilup.size and not ildown.size:
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print 'earllatepicker: Signal lower than noise level!'
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print 'Skip this trace!'
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print ("earllatepicker: Signal lower than noise level!")
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print ("Skip this trace!")
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return LPick, EPick, PickError
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il = min(np.min(ilup) if ilup.size else float('inf'),
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np.min(ildown) if ildown.size else float('inf'))
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@ -143,7 +142,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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FM = None
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if Pick is not None:
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print 'fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...'
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print ("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
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xraw = Xraw[0].data
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xfilt = Xfilt[0].data
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@ -182,15 +181,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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else:
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li1 = index1[0]
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if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
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print 'fmpicker: Onset on unfiltered trace too emergent for first motion determination!'
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print ("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
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P1 = None
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else:
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
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if imax1 == 0:
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
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if imax1 == 0:
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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print ("fmpicker: Zero crossings too close!")
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print ("Skip first motion determination!")
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return FM
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islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
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@ -224,15 +223,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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else:
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li2 = index2[0]
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if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
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print 'fmpicker: Onset on filtered trace too emergent for first motion determination!'
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print ("fmpicker: Onset on filtered trace too emergent for first motion determination!")
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P2 = None
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else:
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
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if imax2 == 0:
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
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if imax2 == 0:
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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print ("fmpicker: Zero crossings too close!")
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print ("Skip first motion determination!")
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return FM
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islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
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@ -256,7 +255,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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elif P1[0] > 0 and P2[0] <= 0:
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FM = '+'
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print 'fmpicker: Found polarity %s' % FM
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print ("fmpicker: Found polarity %s" % FM)
|
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if iplot > 1:
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plt.figure(iplot)
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@ -331,10 +330,10 @@ def getSNR(X, TSNR, t1):
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# get signal window
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isignal = getsignalwin(t, t1, TSNR[2])
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if np.size(inoise) < 1:
|
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print 'getSNR: Empty array inoise, check noise window!'
|
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print ("getSNR: Empty array inoise, check noise window!")
|
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return
|
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elif np.size(isignal) < 1:
|
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print 'getSNR: Empty array isignal, check signal window!'
|
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print ("getSNR: Empty array isignal, check signal window!")
|
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return
|
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# demean over entire waveform
|
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@ -372,7 +371,7 @@ def getnoisewin(t, t1, tnoise, tgap):
|
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inoise, = np.where((t <= max([t1 - tgap, 0])) \
|
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& (t >= max([t1 - tnoise - tgap, 0])))
|
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if np.size(inoise) < 1:
|
||||
print 'getnoisewin: Empty array inoise, check noise window!'
|
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print ("getnoisewin: Empty array inoise, check noise window!")
|
||||
|
||||
return inoise
|
||||
|
||||
@ -396,7 +395,7 @@ def getsignalwin(t, t1, tsignal):
|
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isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
|
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& (t >= t1))
|
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if np.size(isignal) < 1:
|
||||
print 'getsignalwin: Empty array isignal, check signal window!'
|
||||
print ("getsignalwin: Empty array isignal, check signal window!")
|
||||
|
||||
return isignal
|
||||
|
||||
@ -483,8 +482,8 @@ def wadaticheck(pickdic, dttolerance, iplot):
|
||||
|
||||
# calculate vp/vs ratio before check
|
||||
vpvsr = p1[0] + 1
|
||||
print '###############################################'
|
||||
print 'wadaticheck: Average Vp/Vs ratio before check:', vpvsr
|
||||
print ("###############################################")
|
||||
print ("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
|
||||
|
||||
checkedPpicks = []
|
||||
checkedSpicks = []
|
||||
@ -521,18 +520,18 @@ def wadaticheck(pickdic, dttolerance, iplot):
|
||||
|
||||
# calculate vp/vs ratio after check
|
||||
cvpvsr = p2[0] + 1
|
||||
print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
|
||||
print 'wadatacheck: Skipped %d S pick(s).' % ibad
|
||||
print ("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
|
||||
print ("wadatacheck: Skipped %d S pick(s)" % ibad)
|
||||
else:
|
||||
print '###############################################'
|
||||
print 'wadatacheck: Not enough checked S-P times available!'
|
||||
print 'Skip Wadati check!'
|
||||
print ("###############################################")
|
||||
print ("wadatacheck: Not enough checked S-P times available!")
|
||||
print ("Skip Wadati check!")
|
||||
|
||||
checkedonsets = pickdic
|
||||
|
||||
else:
|
||||
print 'wadaticheck: Not enough S-P times available for reliable regression!'
|
||||
print 'Skip wadati check!'
|
||||
print ("wadaticheck: Not enough S-P times available for reliable regression!")
|
||||
print ("Skip wadati check!")
|
||||
wfitflag = 1
|
||||
|
||||
# plot results
|
||||
@ -562,9 +561,9 @@ def wadaticheck(pickdic, dttolerance, iplot):
|
||||
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
|
||||
'''
|
||||
Function to detect spuriously picked noise peaks.
|
||||
Uses envelope to determine, how many samples [per cent] after
|
||||
P onset are below certain threshold, calculated from noise
|
||||
level times noise factor.
|
||||
Uses RMS trace of all 3 components (if available) to determine,
|
||||
how many samples [per cent] after P onset are below certain
|
||||
threshold, calculated from noise level times noise factor.
|
||||
|
||||
: param: X, time series (seismogram)
|
||||
: type: `~obspy.core.stream.Stream`
|
||||
@ -592,47 +591,54 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
|
||||
|
||||
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
|
||||
|
||||
print 'Checking signal length ...'
|
||||
print ("Checking signal length ...")
|
||||
|
||||
x = X[0].data
|
||||
t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
|
||||
if len(X) > 1:
|
||||
# all three components available
|
||||
# make sure, all components have equal lengths
|
||||
ilen = min([len(X[0].data), len(X[1].data), len(X[2].data)])
|
||||
x1 = X[0][0:ilen]
|
||||
x2 = X[1][0:ilen]
|
||||
x3 = X[2][0:ilen]
|
||||
# get RMS trace
|
||||
rms = np.sqrt((np.power(x1, 2) + np.power(x2, 2) + np.power(x3, 2)) / 3)
|
||||
else:
|
||||
x1 = X[0].data
|
||||
rms = np.sqrt(np.power(2, x1))
|
||||
|
||||
t = np.arange(0, ilen / X[0].stats.sampling_rate,
|
||||
X[0].stats.delta)
|
||||
|
||||
# generate envelope function from Hilbert transform
|
||||
y = np.imag(sc.signal.hilbert(x))
|
||||
e = np.sqrt(np.power(x, 2) + np.power(y, 2))
|
||||
# get noise window
|
||||
inoise = getnoisewin(t, pick, TSNR[0], TSNR[1])
|
||||
# get noise window in front of pick plus saftey gap
|
||||
inoise = getnoisewin(t, pick - 0.5, TSNR[0], TSNR[1])
|
||||
# get signal window
|
||||
isignal = getsignalwin(t, pick, TSNR[2])
|
||||
isignal = getsignalwin(t, pick, minsiglength)
|
||||
# calculate minimum adjusted signal level
|
||||
minsiglevel = max(e[inoise]) * nfac
|
||||
minsiglevel = max(rms[inoise]) * nfac
|
||||
# minimum adjusted number of samples over minimum signal level
|
||||
minnum = len(isignal) * minpercent/100
|
||||
# get number of samples above minimum adjusted signal level
|
||||
numoverthr = len(np.where(e[isignal] >= minsiglevel)[0])
|
||||
numoverthr = len(np.where(rms[isignal] >= minsiglevel)[0])
|
||||
|
||||
if numoverthr >= minnum:
|
||||
print 'checksignallength: Signal reached required length.'
|
||||
print ("checksignallength: Signal reached required length.")
|
||||
returnflag = 1
|
||||
else:
|
||||
print 'checksignallength: Signal shorter than required minimum signal length!'
|
||||
print 'Presumably picked noise peak, pick is rejected!'
|
||||
print '(min. signal length required:', minsiglength, 's)'
|
||||
print ("checksignallength: Signal shorter than required minimum signal length!")
|
||||
print ("Presumably picked noise peak, pick is rejected!")
|
||||
print ("(min. signal length required: %s s)" % minsiglength)
|
||||
returnflag = 0
|
||||
|
||||
if iplot == 2:
|
||||
plt.figure(iplot)
|
||||
p1, = plt.plot(t,x, 'k')
|
||||
p2, = plt.plot(t[inoise], e[inoise], 'c')
|
||||
p3, = plt.plot(t[isignal],e[isignal], 'r')
|
||||
p2, = plt.plot(t[inoise], e[inoise])
|
||||
p3, = plt.plot(t[isignal],e[isignal], 'r')
|
||||
p1, = plt.plot(t,rms, 'k')
|
||||
p2, = plt.plot(t[inoise], rms[inoise], 'c')
|
||||
p3, = plt.plot(t[isignal],rms[isignal], 'r')
|
||||
p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \
|
||||
[minsiglevel, minsiglevel], 'g')
|
||||
p5, = plt.plot([pick, pick], [min(x), max(x)], 'b', linewidth=2)
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \
|
||||
'Envelope Signal Window', 'Minimum Signal Level', \
|
||||
[minsiglevel, minsiglevel], 'g', linewidth=2)
|
||||
p5, = plt.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2)
|
||||
plt.legend([p1, p2, p3, p4, p5], ['RMS Data', 'RMS Noise Window', \
|
||||
'RMS Signal Window', 'Minimum Signal Level', \
|
||||
'Onset'], loc='best')
|
||||
plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
|
||||
plt.ylabel('Counts')
|
||||
@ -675,8 +681,8 @@ def checkPonsets(pickdic, dttolerance, iplot):
|
||||
stations.append(key)
|
||||
|
||||
# apply jackknife bootstrapping on variance of P onsets
|
||||
print '###############################################'
|
||||
print 'checkPonsets: Apply jackknife bootstrapping on P-onset times ...'
|
||||
print ("###############################################")
|
||||
print ("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
|
||||
[xjack,PHI_pseudo,PHI_sub] = jackknife(Ppicks, 'VAR', 1)
|
||||
# get pseudo variances smaller than average variances
|
||||
# (times safety factor), these picks passed jackknife test
|
||||
@ -684,7 +690,7 @@ def checkPonsets(pickdic, dttolerance, iplot):
|
||||
# these picks did not pass jackknife test
|
||||
badjk = np.where(PHI_pseudo > 2 * xjack)
|
||||
badjkstations = np.array(stations)[badjk]
|
||||
print 'checkPonsets: %d pick(s) did not pass jackknife test!' % len(badjkstations)
|
||||
print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
|
||||
|
||||
# calculate median from these picks
|
||||
pmedian = np.median(np.array(Ppicks)[ij])
|
||||
@ -696,9 +702,9 @@ def checkPonsets(pickdic, dttolerance, iplot):
|
||||
goodstations = np.array(stations)[igood]
|
||||
badstations = np.array(stations)[ibad]
|
||||
|
||||
print 'checkPonsets: %d pick(s) deviate too much from median!' % len(ibad)
|
||||
print 'checkPonsets: Skipped %d P pick(s) out of %d' % (len(badstations) \
|
||||
+ len(badjkstations), len(stations))
|
||||
print ("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
|
||||
print ("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
|
||||
+ len(badjkstations), len(stations)))
|
||||
|
||||
goodmarker = 'goodPonsetcheck'
|
||||
badmarker = 'badPonsetcheck'
|
||||
@ -765,8 +771,8 @@ def jackknife(X, phi, h):
|
||||
g = len(X) / h
|
||||
|
||||
if type(g) is not int:
|
||||
print 'jackknife: Cannot divide quantity X in equal sized subgroups!'
|
||||
print 'Choose another size for subgroups!'
|
||||
print ("jackknife: Cannot divide quantity X in equal sized subgroups!")
|
||||
print ("Choose another size for subgroups!")
|
||||
return PHI_jack, PHI_pseudo, PHI_sub
|
||||
else:
|
||||
# estimator of undisturbed spot check
|
||||
@ -834,7 +840,7 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
|
||||
|
||||
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
|
||||
|
||||
print 'Check for spuriously picked S onset instead of P onset ...'
|
||||
print ("Check for spuriously picked S onset instead of P onset ...")
|
||||
|
||||
returnflag = 0
|
||||
|
||||
@ -875,9 +881,9 @@ def checkZ4S(X, pick, zfac, checkwin, iplot):
|
||||
# vertical P-coda level must exceed horizontal P-coda level
|
||||
# zfac times encodalevel
|
||||
if zcodalevel < minsiglevel:
|
||||
print 'checkZ4S: Maybe S onset? Skip this P pick!'
|
||||
print ("checkZ4S: Maybe S onset? Skip this P pick!")
|
||||
else:
|
||||
print 'checkZ4S: P onset passes checkZ4S test!'
|
||||
print ("checkZ4S: P onset passes checkZ4S test!")
|
||||
returnflag = 1
|
||||
|
||||
if iplot > 1:
|
||||
|
||||
@ -229,6 +229,9 @@ class Data(object):
|
||||
:param streams:
|
||||
:return:
|
||||
"""
|
||||
|
||||
restflag = 0
|
||||
|
||||
if streams is None:
|
||||
st_raw = self.getWFData()
|
||||
st = st_raw.copy()
|
||||
@ -237,7 +240,7 @@ class Data(object):
|
||||
|
||||
for tr in st:
|
||||
# remove underscores
|
||||
if tr.stats.station[3] == '_':
|
||||
if len(tr.stats.station) > 3 and tr.stats.station[3] == '_':
|
||||
tr.stats.station = tr.stats.station[0:3]
|
||||
dlp = '%s/*.dless' % invdlpath
|
||||
invp = '%s/*.xml' % invdlpath
|
||||
@ -274,6 +277,7 @@ class Data(object):
|
||||
'date': st[
|
||||
i].stats.starttime,
|
||||
'units': "VEL"})
|
||||
restflag = 1
|
||||
except ValueError as e:
|
||||
vmsg = '{0}'.format(e)
|
||||
print(vmsg)
|
||||
@ -304,6 +308,7 @@ class Data(object):
|
||||
st[i].attach_response(inv)
|
||||
st[i].remove_response(output='VEL',
|
||||
pre_filt=prefilt)
|
||||
restflag = 1
|
||||
except ValueError as e:
|
||||
vmsg = '{0}'.format(e)
|
||||
print(vmsg)
|
||||
@ -335,6 +340,7 @@ class Data(object):
|
||||
'units': "VEL"}
|
||||
st[i].simulate(paz_remove=None, pre_filt=prefilt,
|
||||
seedresp=seedresp)
|
||||
restflag = 1
|
||||
except ValueError as e:
|
||||
vmsg = '{0}'.format(e)
|
||||
print(vmsg)
|
||||
@ -347,7 +353,7 @@ class Data(object):
|
||||
print("Go on processing data without source parameter "
|
||||
"determination!")
|
||||
|
||||
return st
|
||||
return st, restflag
|
||||
|
||||
def getEvtData(self):
|
||||
"""
|
||||
|
||||
Loading…
Reference in New Issue
Block a user